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"------------------------------------------------------------\n", " (4,) 1 4-way\n", " (1, 3) 2 independent → 3-way\n", " (2, 2) 2 pairwise → pairwise\n", " (3, 1) 2 3-way → independent\n", " (1, 1, 2) 3 independent → independent → pairwise\n", " (1, 2, 1) 3 independent → pairwise → independent\n", " (2, 1, 1) 3 pairwise → independent → independent\n", " (1, 1, 1, 1) 4 independent → independent → independent → independent\n", "\n", "Compositions of 5: 16 total\n", "Composition Length Interpretation\n", "------------------------------------------------------------\n", " (5,) 1 5-way (full)\n", " (1, 4) 2 independent → 4-way\n", " (2, 3) 2 pairwise → 3-way\n", " (3, 2) 2 3-way → pairwise\n", " (4, 1) 2 4-way → independent\n", " (1, 1, 3) 3 independent → independent → 3-way\n", " (1, 2, 2) 3 independent → pairwise → pairwise\n", " (1, 3, 1) 3 independent → 3-way → independent\n", " (2, 1, 2) 3 pairwise → independent → pairwise\n", " (2, 2, 1) 3 pairwise → pairwise → independent\n", " (3, 1, 1) 3 3-way → independent → independent\n", " (1, 1, 1, 2) 4 independent → independent → independent → pairwise\n", " (1, 1, 2, 1) 4 independent → independent → pairwise → independent\n", " (1, 2, 1, 1) 4 independent → pairwise → independent → independent\n", " (2, 1, 1, 1) 4 pairwise → independent → independent → independent\n", " (1, 1, 1, 1, 1) 5 independent → independent → independent → independent → independent\n", "\n", "conv4d: 8 paths (vs 1 opaque operator)\n", "conv5d: 16 paths (vs 1 opaque operator)\n", "\n", "The 16 paths for conv5d enumerate ALL ways to\n", "traverse a 5-dimensional simplex (pentachoron).\n", "Each path captures a different structural relationship.\n", "Together they form a complete basis for 5d geometry.\n", "\n", "============================================================\n", "TESTING: DualAnchorEmbedding with conv4\n", "============================================================\n", " Parameters: 12,951,816\n", " Conv4 paths: 8\n", " Path 0: (1, 1, 1, 1)\n", " Path 1: (1, 1, 2)\n", " Path 2: (1, 2, 1)\n", " Path 3: (1, 3)\n", " Path 4: (2, 1, 1)\n", " Path 5: (2, 2)\n", " Path 6: (3, 1)\n", " Path 7: (4,)\n", "\n", " Input: anchor_a=(16, 768), anchor_b=(16, 768)\n", " Output: (16, 768)\n", " Output norm: 1.0000\n", "\n", " Path weights (learned):\n", " (1, 1, 1, 1) weight=0.1250\n", " (1, 1, 2) weight=0.1250\n", " (1, 2, 1) weight=0.1250\n", " (1, 3) weight=0.1250\n", " (2, 1, 1) weight=0.1250\n", " (2, 2) weight=0.1250\n", " (3, 1) weight=0.1250\n", " (4,) weight=0.1250\n", "\n", "Done.\n" ] } ], "source": [ "# ============================================================================\n", "# EXPERIMENT: Compositional Convolution via Integer Partitions\n", "#\n", "# Hypothesis: conv4d can be decomposed into the set of ordered compositions\n", "# of 4, each capturing a different factorization of 4-dimensional structure.\n", "# The union of all compositions is a complete, interpretable, well-conditioned\n", "# alternative to opaque N-dimensional convolution.\n", "#\n", "# Applied to: structural differences between two Procrustes-aligned BERTs,\n", "# accumulated in a geometric memory bank.\n", "#\n", "# The conv4 partition set becomes the abstraction to build conv5d (pentachoron).\n", "#\n", "# Compositions of 4: [1,1,1,1], [1,1,2], [1,2,1], [2,1,1], [2,2], [3,1], [1,3], [4]\n", "# Compositions of 5: 16 total paths\n", "# ============================================================================\n", "\n", "import math\n", "from typing import List, Tuple\n", "from itertools import product as cartesian\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPOSITION ENUMERATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def integer_compositions(n: int) -> List[Tuple[int, ...]]:\n", " \"\"\"\n", " Generate all ordered compositions of integer n.\n", " e.g. n=4 → (1,1,1,1), (1,1,2), (1,2,1), (2,1,1), (2,2), (1,3), (3,1), (4)\n", " \"\"\"\n", " if n == 0:\n", " return [()]\n", " if n == 1:\n", " return [(1,)]\n", " result = []\n", " for first in range(1, n + 1):\n", " for rest in integer_compositions(n - first):\n", " result.append((first,) + rest)\n", " return result\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPOSITIONAL CONV PATH\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ConvPath(nn.Module):\n", " \"\"\"\n", " A single composition path through N-dimensional structure.\n", "\n", " Given composition (a, b, c, ...) where a+b+c+...=N:\n", " - Apply conv_{a}d, then conv_{b}d, then conv_{c}d\n", " - Each step processes a different factorization of the input dimensions\n", "\n", " For embedding space: dimensions are abstract (not spatial),\n", " so we use grouped linear projections to simulate N-d convolution\n", " over the embedding's geometric structure.\n", "\n", " conv1d over embeddings = project single dim slices independently\n", " conv2d over embeddings = project pairs of dim slices jointly\n", " conv_kd over embeddings = project k-dim slices jointly\n", " \"\"\"\n", " def __init__(self, composition: Tuple[int, ...], embed_dim: int, hidden_dim: int):\n", " super().__init__()\n", " self.composition = composition\n", " self.total_n = sum(composition)\n", " self.embed_dim = embed_dim\n", " self.hidden_dim = hidden_dim\n", "\n", " # Each step in the composition gets a projection\n", " self.steps = nn.ModuleList()\n", " current_dim = embed_dim\n", " for k in composition:\n", " # conv_kd equivalent: project groups of k dimensions jointly\n", " # Reshape embed into groups, project each group\n", " self.steps.append(nn.ModuleDict({\n", " \"proj\": nn.Linear(current_dim, hidden_dim),\n", " \"group_mix\": nn.Linear(hidden_dim, hidden_dim),\n", " \"norm\": nn.LayerNorm(hidden_dim),\n", " \"k\": nn.Module(), # placeholder for k value\n", " }))\n", " # Store k as buffer\n", " self.steps[-1].k_value = k\n", " current_dim = hidden_dim\n", "\n", " self.output_proj = nn.Linear(hidden_dim, embed_dim)\n", "\n", " def forward(self, x):\n", " \"\"\"\n", " x: (B, embed_dim) — the geometric difference between two anchor views\n", "\n", " Each step reshapes into groups of k dimensions, processes jointly,\n", " then flattens back. This captures k-dimensional correlations at each stage.\n", " \"\"\"\n", " B = x.shape[0]\n", " h = x\n", "\n", " for step in self.steps:\n", " k = step.k_value\n", "\n", " # Project to hidden space\n", " h = step[\"proj\"](h)\n", " h = F.gelu(h)\n", "\n", " # Group mixing: reshape into groups of (hidden_dim // k) × k\n", " # then mix within groups to capture k-dimensional correlations\n", " if k > 1 and self.hidden_dim >= k:\n", " n_groups = self.hidden_dim // k\n", " remainder = self.hidden_dim % k\n", " if n_groups > 0 and remainder == 0:\n", " grouped = h.view(B, n_groups, k)\n", " # k-dimensional interaction within each group\n", " grouped = grouped * torch.softmax(grouped, dim=-1)\n", " h = grouped.view(B, self.hidden_dim)\n", "\n", " h = step[\"group_mix\"](h)\n", " h = F.gelu(h)\n", " h = step[\"norm\"](h)\n", "\n", " return self.output_proj(h)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPOSITIONAL CONV-N MODULE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CompositionalConvN(nn.Module):\n", " \"\"\"\n", " All compositions of N running in parallel.\n", "\n", " Each composition path captures a different factorization of\n", " N-dimensional structure. The outputs are fused via learned\n", " attention weighting.\n", "\n", " For N=4:\n", " 8 paths: [1,1,1,1], [1,1,2], [1,2,1], [2,1,1], [2,2], [1,3], [3,1], [4]\n", "\n", " For N=5 (pentachoron):\n", " 16 paths: all compositions of 5\n", " \"\"\"\n", " def __init__(self, n: int, embed_dim: int, hidden_dim: int,\n", " max_paths: int = 32):\n", " super().__init__()\n", " self.n = n\n", " self.compositions = integer_compositions(n)\n", "\n", " # Optionally limit paths for very large N\n", " if len(self.compositions) > max_paths:\n", " # Keep shortest, longest, and sample middle\n", " sorted_comps = sorted(self.compositions, key=len)\n", " keep = set()\n", " keep.add(sorted_comps[0]) # shortest (N,)\n", " keep.add(sorted_comps[-1]) # longest (1,1,...,1)\n", " # Evenly sample the rest\n", " step = max(len(sorted_comps) // max_paths, 1)\n", " for i in range(0, len(sorted_comps), step):\n", " keep.add(sorted_comps[i])\n", " self.compositions = list(keep)[:max_paths]\n", "\n", " self.paths = nn.ModuleList([\n", " ConvPath(comp, embed_dim, hidden_dim)\n", " for comp in self.compositions\n", " ])\n", "\n", " # Fusion: learned attention over path outputs\n", " self.n_paths = len(self.paths)\n", " self.path_weights = nn.Parameter(torch.ones(self.n_paths) / self.n_paths)\n", " self.fusion = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim),\n", " nn.GELU(),\n", " nn.LayerNorm(embed_dim),\n", " )\n", "\n", " def forward(self, x):\n", " \"\"\"\n", " x: (B, embed_dim)\n", " Returns: (B, embed_dim) — fused output from all composition paths\n", " \"\"\"\n", " outputs = torch.stack([path(x) for path in self.paths], dim=1)\n", " # (B, n_paths, embed_dim)\n", "\n", " weights = F.softmax(self.path_weights, dim=0)\n", " fused = (outputs * weights.view(1, -1, 1)).sum(dim=1)\n", " return self.fusion(fused)\n", "\n", " def path_analysis(self, x):\n", " \"\"\"Diagnostic: return per-path outputs and weights for analysis.\"\"\"\n", " outputs = [path(x) for path in self.paths]\n", " weights = F.softmax(self.path_weights, dim=0)\n", " return {\n", " \"compositions\": self.compositions,\n", " \"weights\": weights.detach().cpu(),\n", " \"outputs\": [o.detach().cpu() for o in outputs],\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC STRUCTURAL MEMORY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class StructuralMemoryBank(nn.Module):\n", " \"\"\"\n", " Memory bank that stores compositional conv decompositions of the\n", " structural differences between two Procrustes-aligned anchor views.\n", "\n", " Instead of anchoring to model layers (like the CLIP memory bank),\n", " this anchors to an embedding spectrum — the full set of compositional\n", " decompositions of the geometric difference between experts.\n", "\n", " Each memory slot stores:\n", " - The fused compositional output (how A and B differ)\n", " - Individual path activations (which factorizations are active)\n", " - Geometric regularity (pentachoron CV on the slot ensemble)\n", " \"\"\"\n", " def __init__(self, embed_dim: int, hidden_dim: int, conv_n: int = 4,\n", " bank_size: int = 64, n_heads: int = 8):\n", " super().__init__()\n", " self.embed_dim = embed_dim\n", " self.bank_size = bank_size\n", "\n", " # Compositional conv for structural difference\n", " self.conv_n = CompositionalConvN(conv_n, embed_dim, hidden_dim)\n", "\n", " # Bank storage\n", " self.register_buffer(\n", " \"bank_slots\", torch.zeros(bank_size, embed_dim))\n", " self.register_buffer(\"n_written\", torch.tensor(0, dtype=torch.long))\n", "\n", " # Read mechanism: cross-attention over bank\n", " self.read_attn = nn.MultiheadAttention(\n", " embed_dim, n_heads, batch_first=True, dropout=0.1)\n", " self.read_norm = nn.LayerNorm(embed_dim)\n", " self.read_ffn = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim * 2),\n", " nn.GELU(),\n", " nn.Linear(embed_dim * 2, embed_dim))\n", " self.read_ffn_norm = nn.LayerNorm(embed_dim)\n", "\n", " def write(self, anchor_a: torch.Tensor, anchor_b: torch.Tensor):\n", " \"\"\"\n", " anchor_a, anchor_b: (B, embed_dim) — Procrustes-aligned embeddings\n", " Computes structural difference, decomposes via compositional conv,\n", " writes to bank.\n", " \"\"\"\n", " # Geometric structural difference — not just subtraction\n", " diff = anchor_a - anchor_b\n", " product = anchor_a * anchor_b\n", " mean = (anchor_a + anchor_b) / 2\n", " structural = torch.cat([diff, product, mean], dim=-1)\n", "\n", " # Project back to embed_dim for conv processing\n", " if not hasattr(self, \"struct_proj\"):\n", " self.struct_proj = nn.Linear(\n", " self.embed_dim * 3, self.embed_dim\n", " ).to(anchor_a.device)\n", " structural = self.struct_proj(structural)\n", "\n", " # Decompose through all composition paths\n", " decomposed = self.conv_n(structural) # (B, embed_dim)\n", "\n", " # Write to bank (circular buffer)\n", " B = decomposed.shape[0]\n", " for i in range(B):\n", " idx = self.n_written % self.bank_size\n", " self.bank_slots[idx] = decomposed[i].detach()\n", " self.n_written += 1\n", "\n", " return decomposed\n", "\n", " def read(self, query: torch.Tensor) -> torch.Tensor:\n", " \"\"\"\n", " query: (B, seq, embed_dim) — what to condition on bank content\n", " Returns: (B, seq, embed_dim) — enriched by bank memory\n", " \"\"\"\n", " n = min(self.n_written.item(), self.bank_size)\n", " if n == 0:\n", " return query\n", "\n", " B = query.shape[0]\n", " bank = self.bank_slots[:n].unsqueeze(0).expand(B, -1, -1)\n", "\n", " # Cross-attend: query reads from bank\n", " residual = query\n", " q_normed = self.read_norm(query)\n", " attended, _ = self.read_attn(q_normed, bank, bank)\n", " query = residual + attended\n", "\n", " residual = query\n", " query = residual + self.read_ffn(self.read_ffn_norm(query))\n", "\n", " return query\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXPERIMENT: TWO-BERT STRUCTURAL EMBEDDING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class DualAnchorEmbedding(nn.Module):\n", " \"\"\"\n", " Full experiment module:\n", " 1. Two frozen BERTs produce aligned embeddings\n", " 2. Compositional conv decomposes their structural difference\n", " 3. Memory bank accumulates decompositions\n", " 4. Output embedding = bank-enriched representation\n", "\n", " The embedding spectrum is the UNION of all compositional views\n", " of how two different trained models interpret the same input.\n", " \"\"\"\n", " def __init__(self, embed_dim: int = 768, hidden_dim: int = 256,\n", " conv_n: int = 4, bank_size: int = 64):\n", " super().__init__()\n", " self.embed_dim = embed_dim\n", "\n", " # Structural memory with compositional conv\n", " self.memory = StructuralMemoryBank(\n", " embed_dim, hidden_dim, conv_n, bank_size)\n", "\n", " # Learner: produces unified embedding from dual-anchor context\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(embed_dim * 2, embed_dim),\n", " nn.GELU(),\n", " nn.LayerNorm(embed_dim))\n", "\n", " # Output head\n", " self.output_head = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim),\n", " nn.GELU(),\n", " nn.Linear(embed_dim, embed_dim))\n", "\n", " def forward(self, anchor_a: torch.Tensor, anchor_b: torch.Tensor):\n", " \"\"\"\n", " anchor_a: (B, embed_dim) — BERT-base aligned\n", " anchor_b: (B, embed_dim) — ModernBERT aligned\n", "\n", " Returns: (B, embed_dim) — unified geometric embedding\n", " \"\"\"\n", " # Write structural difference to memory\n", " structural = self.memory.write(anchor_a, anchor_b)\n", "\n", " # Combine both anchors\n", " combined = self.embed_proj(torch.cat([anchor_a, anchor_b], dim=-1))\n", "\n", " # Read from bank (use combined as query)\n", " enriched = self.memory.read(combined.unsqueeze(1)).squeeze(1)\n", "\n", " # Fuse: structural decomposition + bank-enriched combined\n", " fused = enriched + structural\n", "\n", " return F.normalize(self.output_head(fused), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ANALYSIS TOOLS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def analyze_compositions(n: int):\n", " \"\"\"Print all compositions of n with their structural interpretation.\"\"\"\n", " comps = integer_compositions(n)\n", " print(f\"\\nCompositions of {n}: {len(comps)} total\")\n", " print(f\"{'Composition':<20} {'Length':>6} {'Interpretation'}\")\n", " print(\"-\" * 60)\n", " for comp in sorted(comps, key=lambda c: (len(c), c)):\n", " interp = []\n", " for k in comp:\n", " if k == 1:\n", " interp.append(\"independent\")\n", " elif k == 2:\n", " interp.append(\"pairwise\")\n", " elif k == 3:\n", " interp.append(\"3-way\")\n", " elif k == 4:\n", " interp.append(\"4-way\")\n", " elif k == 5:\n", " interp.append(\"5-way (full)\")\n", " else:\n", " interp.append(f\"{k}-way\")\n", " print(f\" {str(comp):<20} {len(comp):>4} {' → '.join(interp)}\")\n", " return comps\n", "\n", "\n", "def compare_conv4_conv5():\n", " \"\"\"Show the scaling from conv4 to conv5.\"\"\"\n", " print(\"=\" * 60)\n", " print(\"COMPOSITIONAL CONVOLUTION: PARTITION ANALYSIS\")\n", " print(\"=\" * 60)\n", "\n", " c4 = analyze_compositions(4)\n", " c5 = analyze_compositions(5)\n", "\n", " print(f\"\\nconv4d: {len(c4)} paths (vs 1 opaque operator)\")\n", " print(f\"conv5d: {len(c5)} paths (vs 1 opaque operator)\")\n", " print(f\"\\nThe {len(c5)} paths for conv5d enumerate ALL ways to\")\n", " print(f\"traverse a 5-dimensional simplex (pentachoron).\")\n", " print(f\"Each path captures a different structural relationship.\")\n", " print(f\"Together they form a complete basis for 5d geometry.\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " compare_conv4_conv5()\n", "\n", " print(f\"\\n{'='*60}\")\n", " print(\"TESTING: DualAnchorEmbedding with conv4\")\n", " print(f\"{'='*60}\")\n", "\n", " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", " model = DualAnchorEmbedding(\n", " embed_dim=768, hidden_dim=256, conv_n=4, bank_size=64\n", " ).to(device)\n", "\n", " n_params = sum(p.numel() for p in model.parameters())\n", " print(f\" Parameters: {n_params:,}\")\n", " print(f\" Conv4 paths: {len(model.memory.conv_n.compositions)}\")\n", " for i, comp in enumerate(model.memory.conv_n.compositions):\n", " print(f\" Path {i}: {comp}\")\n", "\n", " # Simulate two aligned BERT embeddings\n", " B = 16\n", " anchor_a = F.normalize(torch.randn(B, 768, device=device), dim=-1)\n", " anchor_b = F.normalize(torch.randn(B, 768, device=device), dim=-1)\n", "\n", " # Forward\n", " output = model(anchor_a, anchor_b)\n", " print(f\"\\n Input: anchor_a={tuple(anchor_a.shape)}, anchor_b={tuple(anchor_b.shape)}\")\n", " print(f\" Output: {tuple(output.shape)}\")\n", " print(f\" Output norm: {output.norm(dim=-1).mean():.4f}\")\n", "\n", " # Path analysis\n", " analysis = model.memory.conv_n.path_analysis(anchor_a - anchor_b)\n", " print(f\"\\n Path weights (learned):\")\n", " for comp, w in zip(analysis[\"compositions\"], analysis[\"weights\"]):\n", " print(f\" {str(comp):<20} weight={w:.4f}\")\n", "\n", " print(\"\\nDone.\")" ] }, { "cell_type": "markdown", "source": [ "# dual anchored procrustes embedding hub bert" ], "metadata": { "id": "QOYafE-wP2Yr" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# COMPOSITIONAL CONV EXPERIMENT: Full Pipeline\n", "#\n", "# 1. Extract BERT-base + ModernBERT-large pooled embeddings on CC12M captions\n", "# 2. Procrustes-align both to shared 768-dim space\n", "# 3. Train DualAnchorEmbedding with compositional conv4 decomposition\n", "# 4. Losses: dual InfoNCE + pentachoron CV + running SVD alignment\n", "# 5. Analyze: which composition paths does the model learn to weight?\n", "#\n", "# This tests whether integer partition decomposition of structural\n", "# differences between two aligned models produces a useful unified\n", "# embedding space.\n", "# ============================================================================\n", "\n", "import math\n", "import os\n", "import time\n", "import json\n", "from dataclasses import dataclass\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from tqdm import tqdm\n", "\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"COMPOSITIONAL CONV EXPERIMENT\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STEP 1: EXTRACT EMBEDDINGS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class ExtractConfig:\n", " n_samples: int = 20000\n", " batch_size: int = 64\n", " max_len_bert: int = 512\n", " max_len_modern: int = 512\n", " min_caption_len: int = 50\n", " cache_dir: str = \"/home/claude/comp_conv_cache\"\n", "\n", "ECFG = ExtractConfig()\n", "\n", "\n", "def load_captions(n, min_len=50):\n", " from datasets import load_dataset\n", " print(f\"\\n Loading captions (n={n})...\")\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > min_len:\n", " captions.append(cap)\n", " if len(captions) >= n:\n", " break\n", " print(f\" Got {len(captions)} captions\")\n", " return captions\n", "\n", "\n", "@torch.no_grad()\n", "def extract_embeddings(model_name, captions, max_len, batch_size=64):\n", " from transformers import AutoModel, AutoTokenizer\n", " print(f\"\\n Extracting from {model_name}...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " dim = model.config.hidden_size\n", " print(f\" dim={dim}, {sum(p.numel() for p in model.parameters()):,} params\")\n", "\n", " all_embeds = []\n", " for i in tqdm(range(0, len(captions), batch_size), desc=f\" {model_name.split('/')[-1]}\"):\n", " batch = captions[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " mask = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " all_embeds.append(pooled.cpu())\n", "\n", " embeds = torch.cat(all_embeds)\n", " print(f\" Shape: {embeds.shape}\")\n", "\n", " del model\n", " torch.cuda.empty_cache()\n", " return embeds\n", "\n", "\n", "def extract_or_load():\n", " os.makedirs(ECFG.cache_dir, exist_ok=True)\n", " bert_path = os.path.join(ECFG.cache_dir, \"bert_base.pt\")\n", " modern_path = os.path.join(ECFG.cache_dir, \"modern_bert.pt\")\n", " caps_path = os.path.join(ECFG.cache_dir, \"captions.json\")\n", "\n", " if os.path.exists(bert_path) and os.path.exists(modern_path):\n", " print(\"\\n Loading cached embeddings...\")\n", " bert_emb = torch.load(bert_path, weights_only=True)\n", " modern_emb = torch.load(modern_path, weights_only=True)\n", " with open(caps_path) as f:\n", " captions = json.load(f)\n", " print(f\" BERT-base: {bert_emb.shape}\")\n", " print(f\" ModernBERT: {modern_emb.shape}\")\n", " return bert_emb, modern_emb, captions\n", "\n", " captions = load_captions(ECFG.n_samples, ECFG.min_caption_len)\n", "\n", " bert_emb = extract_embeddings(\n", " \"google-bert/bert-base-uncased\", captions,\n", " ECFG.max_len_bert, ECFG.batch_size)\n", "\n", " modern_emb = extract_embeddings(\n", " \"answerdotai/ModernBERT-base\", captions,\n", " ECFG.max_len_modern, ECFG.batch_size)\n", "\n", " torch.save(bert_emb, bert_path)\n", " torch.save(modern_emb, modern_path)\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", "\n", " return bert_emb, modern_emb, captions\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STEP 2: PROCRUSTES ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " \"\"\"\n", " Align source → target space via whitened Procrustes.\n", " Returns rotation matrix and means for inference-time alignment.\n", " Handles dimension mismatch: projects larger down to smaller.\n", " \"\"\"\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float()\n", " T = target[:N].float()\n", "\n", " s_mean = S.mean(0, keepdim=True)\n", " t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean\n", " Tc = T - t_mean\n", "\n", " d_s, d_t = Sc.shape[1], Tc.shape[1]\n", "\n", " # Align dimensions\n", " projection = None\n", " if d_s > d_t:\n", " _, _, Vt = torch.linalg.svd(Sc, full_matrices=False)\n", " projection = Vt[:d_t].T\n", " Sc = Sc @ projection\n", " elif d_s < d_t:\n", " pad = torch.zeros(N, d_t - d_s)\n", " Sc = torch.cat([Sc, pad], dim=1)\n", " projection = \"pad\"\n", "\n", " cos_before = F.cosine_similarity(Sc, Tc[:, :Sc.shape[1]], dim=-1).mean().item()\n", "\n", " # SVD for rotation\n", " cross = Tc.T @ Sc\n", " U, S_vals, Vt = torch.linalg.svd(cross, full_matrices=False)\n", " R = U @ Vt\n", "\n", " Sc_rotated = Sc @ R.T\n", " cos_after = F.cosine_similarity(Sc_rotated, Tc, dim=-1).mean().item()\n", "\n", " print(f\" Procrustes: cos {cos_before:.4f} → {cos_after:.4f}\")\n", "\n", " return {\n", " \"rotation\": R,\n", " \"source_mean\": s_mean.squeeze(0),\n", " \"target_mean\": t_mean.squeeze(0),\n", " \"projection\": projection,\n", " \"cos_before\": cos_before,\n", " \"cos_after\": cos_after,\n", " \"d_target\": d_t,\n", " }\n", "\n", "\n", "def apply_alignment(embeddings, alignment):\n", " \"\"\"Apply stored Procrustes alignment to embeddings.\"\"\"\n", " x = embeddings.float() - alignment[\"source_mean\"]\n", "\n", " if alignment[\"projection\"] is not None:\n", " if alignment[\"projection\"] == \"pad\":\n", " d_t = alignment[\"d_target\"]\n", " d_s = x.shape[1]\n", " if d_s < d_t:\n", " pad = torch.zeros(x.shape[0], d_t - d_s)\n", " x = torch.cat([x, pad], dim=1)\n", " else:\n", " x = x @ alignment[\"projection\"]\n", "\n", " x = x @ alignment[\"rotation\"].T\n", " return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STEP 3: COMPOSITIONAL CONV (from experiment file)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def integer_compositions(n):\n", " if n == 0: return [()]\n", " if n == 1: return [(1,)]\n", " result = []\n", " for first in range(1, n + 1):\n", " for rest in integer_compositions(n - first):\n", " result.append((first,) + rest)\n", " return result\n", "\n", "\n", "class ConvPath(nn.Module):\n", " def __init__(self, composition, embed_dim, hidden_dim):\n", " super().__init__()\n", " self.composition = composition\n", " self.embed_dim = embed_dim\n", " self.hidden_dim = hidden_dim\n", "\n", " self.steps = nn.ModuleList()\n", " current_dim = embed_dim\n", " for k in composition:\n", " self.steps.append(nn.ModuleDict({\n", " \"proj\": nn.Linear(current_dim, hidden_dim),\n", " \"group_mix\": nn.Linear(hidden_dim, hidden_dim),\n", " \"norm\": nn.LayerNorm(hidden_dim),\n", " }))\n", " self.steps[-1].k_value = k\n", " current_dim = hidden_dim\n", "\n", " self.output_proj = nn.Linear(hidden_dim, embed_dim)\n", "\n", " def forward(self, x):\n", " B = x.shape[0]\n", " h = x\n", " for step in self.steps:\n", " k = step.k_value\n", " h = F.gelu(step[\"proj\"](h))\n", " if k > 1 and self.hidden_dim >= k:\n", " n_groups = self.hidden_dim // k\n", " if n_groups > 0 and self.hidden_dim % k == 0:\n", " grouped = h.view(B, n_groups, k)\n", " grouped = grouped * torch.softmax(grouped, dim=-1)\n", " h = grouped.view(B, self.hidden_dim)\n", " h = F.gelu(step[\"group_mix\"](h))\n", " h = step[\"norm\"](h)\n", " return self.output_proj(h)\n", "\n", "\n", "class CompositionalConvN(nn.Module):\n", " def __init__(self, n, embed_dim, hidden_dim):\n", " super().__init__()\n", " self.n = n\n", " self.compositions = integer_compositions(n)\n", " self.paths = nn.ModuleList([\n", " ConvPath(comp, embed_dim, hidden_dim) for comp in self.compositions])\n", " self.n_paths = len(self.paths)\n", " self.path_weights = nn.Parameter(torch.ones(self.n_paths) / self.n_paths)\n", " self.fusion = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim), nn.GELU(), nn.LayerNorm(embed_dim))\n", "\n", " def forward(self, x):\n", " outputs = torch.stack([path(x) for path in self.paths], dim=1)\n", " weights = F.softmax(self.path_weights, dim=0)\n", " fused = (outputs * weights.view(1, -1, 1)).sum(dim=1)\n", " return self.fusion(fused)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STEP 4: DUAL ANCHOR MODEL + TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class DualAnchorModel(nn.Module):\n", " def __init__(self, embed_dim=768, hidden_dim=256, conv_n=4):\n", " super().__init__()\n", " self.embed_dim = embed_dim\n", " self.conv = CompositionalConvN(conv_n, embed_dim, hidden_dim)\n", " self.combine = nn.Sequential(\n", " nn.Linear(embed_dim * 3, embed_dim),\n", " nn.GELU(), nn.LayerNorm(embed_dim))\n", " self.output = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim),\n", " nn.GELU(), nn.Linear(embed_dim, embed_dim))\n", "\n", " def forward(self, anchor_a, anchor_b):\n", " diff = anchor_a - anchor_b\n", " structural = self.conv(diff)\n", " mean = (anchor_a + anchor_b) / 2\n", " combined = self.combine(torch.cat([structural, anchor_a, anchor_b], dim=-1))\n", " return F.normalize(self.output(combined + mean), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC LOSSES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def pentachoron_cv_loss(embeddings, target=0.20, n_samples=16):\n", " B = embeddings.shape[0]\n", " if B < 5:\n", " return torch.tensor(0.0, device=embeddings.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=embeddings.device)[:5]\n", " v2 = cayley_menger_vol2(embeddings[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "def svd_alignment(a, b):\n", " A = F.normalize(a.float(), dim=-1)\n", " B_e = F.normalize(b.float(), dim=-1)\n", " A = A - A.mean(0, keepdim=True)\n", " B_e = B_e - B_e.mean(0, keepdim=True)\n", " N, D = A.shape\n", " try:\n", " if N < D:\n", " S = torch.linalg.svdvals(A @ B_e.T)\n", " else:\n", " S = torch.linalg.svdvals(A.T @ B_e)\n", " except Exception:\n", " return torch.tensor(0.0, device=a.device, requires_grad=True)\n", " return 1.0 - S.sum() / (math.sqrt(N) * D)\n", "\n", "\n", "def pentachoron_cv_metric(embeddings, n_samples=200):\n", " B = embeddings.shape[0]\n", " if B < 5:\n", " return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=embeddings.device)[:5]\n", " v2 = cayley_menger_vol2(embeddings[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0:\n", " vols.append(v)\n", " if len(vols) < 10:\n", " return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class TrainConfig:\n", " epochs: int = 20\n", " batch_size: int = 256\n", " lr: float = 3e-4\n", " weight_decay: float = 0.01\n", " grad_clip: float = 1.0\n", " # Loss weights\n", " infonce_a_weight: float = 1.0\n", " infonce_b_weight: float = 1.0\n", " cv_weight: float = 0.1\n", " svd_a_weight: float = 0.05\n", " svd_b_weight: float = 0.05\n", "\n", "TCFG = TrainConfig()\n", "\n", "\n", "def train():\n", " # ── Extract ──\n", " bert_emb, modern_emb, captions = extract_or_load()\n", "\n", " # ── Align both to shared 768-dim space ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PROCRUSTES ALIGNMENT\")\n", " print(f\"{'='*65}\")\n", "\n", " # ModernBERT-base is also 768-dim, so alignment is direct\n", " d_bert = bert_emb.shape[1]\n", " d_modern = modern_emb.shape[1]\n", " d_shared = min(d_bert, d_modern)\n", " print(f\" BERT-base: {d_bert}-dim\")\n", " print(f\" ModernBERT-base: {d_modern}-dim\")\n", " print(f\" Shared space: {d_shared}-dim\")\n", "\n", " print(f\"\\n Aligning BERT-base → shared:\")\n", " align_bert = procrustes_align(bert_emb, modern_emb)\n", " bert_aligned = apply_alignment(bert_emb, align_bert)\n", "\n", " print(f\" Aligning ModernBERT → shared:\")\n", " align_modern = procrustes_align(modern_emb, modern_emb) # identity, but keeps API consistent\n", " modern_aligned = apply_alignment(modern_emb, align_modern)\n", "\n", " # Verify alignment\n", " N = min(5000, bert_aligned.shape[0])\n", " cos = F.cosine_similarity(bert_aligned[:N], modern_aligned[:N], dim=-1).mean().item()\n", " print(f\"\\n Post-alignment cosine: {cos:.4f}\")\n", "\n", " # Split train/val\n", " n_val = 2000\n", " n_train = bert_aligned.shape[0] - n_val\n", " train_a = bert_aligned[:n_train].to(DEVICE)\n", " train_b = modern_aligned[:n_train].to(DEVICE)\n", " val_a = bert_aligned[n_train:].to(DEVICE)\n", " val_b = modern_aligned[n_train:].to(DEVICE)\n", " print(f\" Train: {n_train}, Val: {n_val}\")\n", "\n", " # ── Build model ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"MODEL\")\n", " print(f\"{'='*65}\")\n", "\n", " model = DualAnchorModel(\n", " embed_dim=d_shared, hidden_dim=256, conv_n=4).to(DEVICE)\n", " n_params = sum(p.numel() for p in model.parameters())\n", " print(f\" Parameters: {n_params:,}\")\n", " print(f\" Conv4 paths: {len(model.conv.compositions)}\")\n", " for i, comp in enumerate(model.conv.compositions):\n", " print(f\" {i}: {comp}\")\n", "\n", " # ── Train ──\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({TCFG.epochs} epochs)\")\n", " print(f\"{'='*65}\")\n", "\n", " optimizer = torch.optim.AdamW(model.parameters(), lr=TCFG.lr,\n", " weight_decay=TCFG.weight_decay)\n", " n_batches = n_train // TCFG.batch_size\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=n_batches * TCFG.epochs, eta_min=1e-6)\n", "\n", " all_metrics = {\"epochs\": [], \"path_weights\": []}\n", "\n", " for epoch in range(TCFG.epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " losses = {\"total\": 0, \"nce_a\": 0, \"nce_b\": 0, \"cv\": 0, \"svd_a\": 0, \"svd_b\": 0}\n", " metrics = {\"acc_a\": 0, \"acc_b\": 0}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, TCFG.batch_size):\n", " idx = perm[i:i+TCFG.batch_size]\n", " if len(idx) < 8:\n", " continue\n", " a = train_a[idx]\n", " b = train_b[idx]\n", "\n", " emb = model(a, b)\n", "\n", " # 5 losses\n", " l_nce_a, acc_a = infonce(emb, a)\n", " l_nce_b, acc_b = infonce(emb, b)\n", " l_cv = pentachoron_cv_loss(emb)\n", " l_svd_a = svd_alignment(emb, a)\n", " l_svd_b = svd_alignment(emb, b)\n", "\n", " loss = (TCFG.infonce_a_weight * l_nce_a +\n", " TCFG.infonce_b_weight * l_nce_b +\n", " TCFG.cv_weight * l_cv +\n", " TCFG.svd_a_weight * l_svd_a +\n", " TCFG.svd_b_weight * l_svd_b)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), TCFG.grad_clip)\n", " optimizer.step()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " losses[\"total\"] += loss.item()\n", " losses[\"nce_a\"] += l_nce_a.item()\n", " losses[\"nce_b\"] += l_nce_b.item()\n", " losses[\"cv\"] += l_cv.item()\n", " metrics[\"acc_a\"] += acc_a\n", " metrics[\"acc_b\"] += acc_b\n", " n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", "\n", " # Path weights\n", " pw = F.softmax(model.conv.path_weights, dim=0).detach().cpu().tolist()\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " val_emb = model(val_a, val_b)\n", " _, v_acc_a = infonce(val_emb, val_a)\n", " _, v_acc_b = infonce(val_emb, val_b)\n", " v_cv = pentachoron_cv_metric(val_emb)\n", "\n", " summary = {\n", " \"epoch\": epoch + 1,\n", " \"loss\": losses[\"total\"] / d,\n", " \"acc_a\": metrics[\"acc_a\"] / d,\n", " \"acc_b\": metrics[\"acc_b\"] / d,\n", " \"val_acc_a\": v_acc_a,\n", " \"val_acc_b\": v_acc_b,\n", " \"val_cv\": v_cv,\n", " }\n", " all_metrics[\"epochs\"].append(summary)\n", " all_metrics[\"path_weights\"].append(pw)\n", "\n", " # Path weight string\n", " top_paths = sorted(zip(model.conv.compositions, pw), key=lambda x: -x[1])[:3]\n", " top_str = \" \".join(f\"{str(c)}={w:.3f}\" for c, w in top_paths)\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s \"\n", " f\"loss={summary['loss']:.4f} \"\n", " f\"acc_a={summary['acc_a']:.3f}/{summary['val_acc_a']:.3f} \"\n", " f\"acc_b={summary['acc_b']:.3f}/{summary['val_acc_b']:.3f} \"\n", " f\"cv={summary['val_cv']:.3f} \"\n", " f\"top: {top_str}\")\n", "\n", " # ── Final analysis ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"FINAL PATH WEIGHT ANALYSIS\")\n", " print(f\"{'='*65}\")\n", "\n", " pw = F.softmax(model.conv.path_weights, dim=0).detach().cpu()\n", " for comp, w in sorted(zip(model.conv.compositions, pw.tolist()), key=lambda x: -x[1]):\n", " bar = \"█\" * int(w * 50)\n", " labels = {1: \"indep\", 2: \"pair\", 3: \"3-way\", 4: \"4-way\"}\n", " interp = \" → \".join(labels.get(k, f\"{k}-way\") for k in comp)\n", " print(f\" {str(comp):<20} {w:.4f} {bar} ({interp})\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"TRANSITIVITY TEST\")\n", " print(f\"{'='*65}\")\n", " print(\" Can the learned embeddings align with models never seen during training?\")\n", "\n", " with torch.no_grad():\n", " # Generate embeddings for val set\n", " val_emb = model(val_a, val_b).cpu()\n", "\n", " # Direct cosine with each anchor\n", " cos_a = F.cosine_similarity(val_emb, val_a.cpu(), dim=-1).mean().item()\n", " cos_b = F.cosine_similarity(val_emb, val_b.cpu(), dim=-1).mean().item()\n", "\n", " # Midpoint check — is the embedding between the two anchors?\n", " midpoint = F.normalize((val_a.cpu() + val_b.cpu()) / 2, dim=-1)\n", " cos_mid = F.cosine_similarity(val_emb, midpoint, dim=-1).mean().item()\n", "\n", " # CV of the output space\n", " cv_output = pentachoron_cv_metric(val_emb[:1000])\n", " cv_anchor_a = pentachoron_cv_metric(val_a.cpu()[:1000])\n", " cv_anchor_b = pentachoron_cv_metric(val_b.cpu()[:1000])\n", "\n", " print(f\" cos(output, BERT-base aligned): {cos_a:.4f}\")\n", " print(f\" cos(output, ModernBERT aligned): {cos_b:.4f}\")\n", " print(f\" cos(output, anchor midpoint): {cos_mid:.4f}\")\n", " print(f\" CV output: {cv_output:.4f}\")\n", " print(f\" CV BERT-base: {cv_anchor_a:.4f}\")\n", " print(f\" CV ModernBERT: {cv_anchor_b:.4f}\")\n", "\n", " # Save\n", " os.makedirs(ECFG.cache_dir, exist_ok=True)\n", " with open(os.path.join(ECFG.cache_dir, \"results.json\"), \"w\") as f:\n", " json.dump(all_metrics, f, indent=2, default=str)\n", " torch.save(model.state_dict(), os.path.join(ECFG.cache_dir, \"model.pt\"))\n", " print(f\"\\n Saved to {ECFG.cache_dir}/\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "af7741af1af84bd7a81fbb7d3df7a41a", "f890ba6cefd04309be792af187a14778", "ed379350acdf445aa85df8ffb3f7a1f1", "ab6bb3be570143c89474b6be3082fb5b", "cfe32c9e840c42bbbef91c80b7b8b886", "e9a4b0f6b6eb44e5aee20c32efeea4ab", "e932c785787047a98f376680c2928142", "247fe9ef5fc64d25bde33a8116724852", "34ffabfc9e164f6293bac0bbfac25b14", "e126fdbf7d2b4b1398cf92e696170a61", "d65e49eaa33e454486f1a7f9624d6d06", "6b4725bd99ee4693b47742b4b95033e5", "784f4114300447a292918ae6cc8274dc", "d641312e61fe4238bdfd40819b512383", "19ee6a55eace4f76aa561fe0078b3e18", "b7152bd38a6d4388b99acd735364be70", "4c24f92ca35b411f9a2b554523c526c0", "91dd7aab738a4c928f93508641381d5b", "088f7a64855744d3a195f5057e8053b2", "72f5b45060a845eaa5776557e0ddcf47", "8e9b2ef85269406a91b7b6e593fce474", "507c6f4a0089432da303ef03dbbe93ea", "b708de183007485a84fce02f0f918398", "ce78e2bc90ad423d9180680f724d7107", "43d9a583867d4d45a6a3291ec6bb3581", "e4131698cac54bfba1100e8d8328aa97", "3ae0042c5c89410295860b1ff13c7db3", "cf806b5ba0aa40a78be556958c52d9bf", "dad22612bf4e4958bc0e7af71d35308e", "3121edee7f724f82bc3921482672f841", "ce763fde05bb456cb6c9220f10757607", "591022de240148fc88f5f02c007ce6ea", "2f4c623aecf44199b3cddef3fcc4140e", "216c15222c874c359070555738926819", "3158000d5861479097563eea8c76029e", "686b256c884a4ecdba4115c417192e14", "876b5a1218a64365ad500bd53e1850fe", "d15678c48b684b81b7daf2f98f161d26", "1dba758a1c164477be0e3835c498accd", "32286444007e41118322a8170daba070", "177b1c0d87bb424797a8c371c7624ec8", "70797a5f20a54b9099adf3aa23549fd1", "a4c9cbf026004f59bb547584584ed47d", "c28367a57ae84981acb2b4173c28cb45", "9989d1226f6a420a9ba12cfb39eb6bd3", "a669a3fd19fa47bfaf03ed56515c68ec", "058e48a251d348378acbd5a51df74e86", "ef1c325ae9d545efb78c569d1ded84c6", "6025ac339fd34cd38fc444cdf44299f3", "49475ec1c57e4da3a4c149de7a68b787", "a8001a46a8e34afca224c48e13a751e8", "7ed9424968cd48c69762e2c2796293fa", "964f870da0dd41d9879cda99e2583c64", "c2e096c6cc174454a9c7ea5b0c611ed9", "ccac7943ff9b42eba4fefc79cf8e93aa", "249e4535faaa4866a374f6fea46b57a2", "0ef63ff1dc114ebeb4e5f3388e396faa", "57d2d9dcbf7c497b9b07312688914a75", "0c8bb082dd924f43809a544fbf6e22ef", "b77cb0b275d8476bae59fc6c17d47115", "dc088f102c904774ac170cd157a18cf0", "03599554e1214fa59c53e3a65075ff55", "0e84ed64f2f44ed69213374cc614d494", "cbc3ef4fc6e1461db49506d61eee4f21", "55e78b09dde14e1182e27ff963b3f472", "767d6e11ebc743b68d23e62960686e61", "5a91836f467e46959f1b80db2ca6afea", "b840c5497d154057b7eebe02f47ccc51", "1672c7add2d544428851a21ed996dd0a", "4dc0c89c1a724f159f1ca1473dac9f5f", "098c25ce400b4293980b2bdbcc9f2bf9", "23f8dc075ee94518927e405921fb5d4b", "098c1f83a8f24c41ab4cd7e229a56754", "6a3947c2b7c744bfbed33e605327e1e7", "c984888d8311469ebf2c66d83c98c749", "c665765cb32942448634fb62540aff20", "913f6311e9bf4058a2d301295e01aae7", "eed503f995d74c449acfb217ce0acd7d", "584923fc781040288e04c2df23ba6207", "21209e6cc4af496c96a918ee64f7e114", "7c82222cb6e54855935571450a66d72c", "2b9bf9f8d67f4f31ba5f592f68431da8", "06a18d9b194f4d5783ba45f2b674cded", "7f237b344f734beb9dbe005c33a553dd", "52cdc94fe2b84df88e54a7c34f5fd38f", "dfa6dce60ea44b3d8b3a264b90684fc0", "261b5f47bc1748b98cca6125a96fc8e0", "c8efdf5df992463e9cca11c28186d33d", "2c869b1b80574876bb99de5b3535fd2c", "ba8969ad55e44c638e115599042a7b37", "8a4fbfc103be43c28d430a40de7d2213", "e1ae0da775fb4af1bbcb3387c6c3deac", "e945c3c437ca4ac69901538823b69203", "698bca7a9f6c4b0c81e047e49a9aa046", "45a1b40fa4a0460abc61e6d82ce14a4c", "5c6ac9d91f4245c6b96ad0a5d9102e41", "bb9870743c2c49699d04fc1802b72577", "78b2e7158eb94aedba001fbd14fe98c3", "b220cdda21a94f2dbaa3b627fbeb78ee", "af2a792cfc4a4886afb14f23a69e8e32", "017d834170154d6caa6fe3fbcad87cc8", "f94b989f3400401fb93db36bb4ae1e43", "ef1197d0a7d24adcafb7f62814f2fa64", "4e22cb7b7eb8437d94a5f3cd84c00cad", "82fdb2e5fc3540c2adcd4717520b4f05", "2e55bc310fd94b5b88a11ed8844cf989", "e57da4bd40da4427a224607b5a17fcdf", "8c40cdfb5ae544dea8f1458da2d35cb1", "158bc2ca4b304e6e94fcd839a30d47f4", "f5e3d0180b7f418693ca40d042674d97", "a085e226619c41f6853fef4568929453", "4fc62e7e1e19420aa7974190a48b76ac", "f419ed984fe14816bf27f313ae93b7c7", "2cfb05e56c964bbfae9b4eab1935c4e7", "ca7dc6d7370347699c4fdf7a210aa898", "51cc7cb998b24fa5a62722a363f18fd2", "62f7292135d24aeeb9eb4ca2f8fb93e9", "cc3251b8ba30447abf38e3d1804e4c45", "0e28de4e717e4d26a003b1da684a9b50", "15878b16affc454d8edf48f9ade837bf", "d1952bd013b742b988caf768547b648f", "d8491c9e16c44cb686845d39f8ac392d", "772fbd960f124a39b87531b2661ba5b8", "4b73d09efe854ed99bf962c1f6791d4b", "425902f67d044118a72cefcb35a4a7da", "39804dbc2f224ff49fe4dbaba666bdab", "cce9161655c943538a5c485a6c5aeff4", "044ce8c81d2a4d67beae9f945068e5cb", "4ca65781df6242a6ab2b1f72a1de9f4a", "8158cfa78ddb4cf6a6693cb026347a6a", "fbdf4d1087614c03af3037b03208df82", "c4dbefe5d8e6410db7b2228d897766f6", "91a81ac85cd04855b64b721830cb1ac2", "6ceef1cd00314bdaa54537cb4ccd8634", "6a345a7d08b54545a02a4bd706705d9a", "02e8c91e46af4e6bbab16b9a80b675a8", "632c821a403d4cbdaa18e9c96848bdfc", "108db55efdf049efb551e9397c3d0072", "c7d4f9bea5474958ad5975451c65fdd4", "8db8d51ee82f408793ff80a62c7654eb", "20f3700205444e24ae3562944a5dadad", "dec81f3e632045ef9a2b91ec1febb795", "a23face7e21b44918c9f1edea5053c08" ] }, "id": "fs6DkaDpPmbK", "outputId": "7eb718f5-55b7-48ae-d173-ed93eafdf6b8" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "COMPOSITIONAL CONV EXPERIMENT\n", "=================================================================\n", " Device: cuda\n", "\n", " Loading captions (n=20000)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "af7741af1af84bd7a81fbb7d3df7a41a" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " Got 20000 captions\n", "\n", " Extracting from google-bert/bert-base-uncased...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/570 [00:00 min_len:\n", " captions.append(cap)\n", " if len(captions) >= n:\n", " break\n", " print(f\" Got {len(captions)} captions\")\n", " return captions\n", "\n", "\n", "@torch.no_grad()\n", "def extract_one(model_name, short_name, captions, max_len, batch_size):\n", " from transformers import AutoModel, AutoTokenizer\n", " print(f\"\\n Extracting: {short_name} ({model_name})...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " dim = model.config.hidden_size\n", " print(f\" dim={dim}, {sum(p.numel() for p in model.parameters()):,} params\")\n", "\n", " all_emb = []\n", " for i in tqdm(range(0, len(captions), batch_size), desc=f\" {short_name}\"):\n", " batch = captions[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " # Mean pool\n", " hs = out.last_hidden_state\n", " mask = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (hs * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", "\n", " emb = torch.cat(all_emb)\n", " print(f\" Shape: {emb.shape}\")\n", " del model\n", " torch.cuda.empty_cache()\n", " return emb\n", "\n", "\n", "def extract_all():\n", " os.makedirs(CFG.cache_dir, exist_ok=True)\n", " caps_path = os.path.join(CFG.cache_dir, \"captions.json\")\n", "\n", " # Check cache\n", " all_cached = all(\n", " os.path.exists(os.path.join(CFG.cache_dir, f\"{short}.pt\"))\n", " for _, short in MODELS)\n", "\n", " if all_cached:\n", " print(\"\\n Loading cached embeddings...\")\n", " embeds = {}\n", " for _, short in MODELS:\n", " embeds[short] = torch.load(\n", " os.path.join(CFG.cache_dir, f\"{short}.pt\"), weights_only=True)\n", " print(f\" {short}: {embeds[short].shape}\")\n", " with open(caps_path) as f:\n", " captions = json.load(f)\n", " return embeds, captions\n", "\n", " captions = load_captions(CFG.n_samples, CFG.min_caption_len)\n", "\n", " embeds = {}\n", " for model_name, short in MODELS:\n", " emb = extract_one(model_name, short, captions,\n", " CFG.max_len, CFG.batch_size)\n", " # Handle dimension mismatch — ALBERT-base-v2 is 768 but some\n", " # models might differ. Pad/truncate to 768.\n", " if emb.shape[1] != 768:\n", " print(f\" Adjusting {short} from {emb.shape[1]} to 768\")\n", " if emb.shape[1] < 768:\n", " emb = F.pad(emb, (0, 768 - emb.shape[1]))\n", " else:\n", " emb = emb[:, :768]\n", " embeds[short] = emb\n", " torch.save(emb, os.path.join(CFG.cache_dir, f\"{short}.pt\"))\n", "\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", "\n", " return embeds, captions\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES ALIGNMENT (all to first model's space)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float()\n", " T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True)\n", " t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean\n", " Tc = T - t_mean\n", "\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", "\n", " N_s, D = Sc.shape\n", " try:\n", " if N_s < D:\n", " cross = Tc.T @ Sc\n", " else:\n", " cross = Tc.T @ Sc\n", " U, _, Vt = torch.linalg.svd(cross, full_matrices=False)\n", " R = U @ Vt\n", " except Exception:\n", " R = torch.eye(D)\n", "\n", " Sc_rot = Sc @ R.T\n", " cos_after = F.cosine_similarity(Sc_rot, Tc, dim=-1).mean().item()\n", "\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"target_mean\": t_mean.squeeze(0),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "\n", "def apply_align(emb, alignment):\n", " x = emb.float() - alignment[\"source_mean\"]\n", " return x @ alignment[\"rotation\"].T\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPOSITIONAL CONV5D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def integer_compositions(n):\n", " if n == 0: return [()]\n", " if n == 1: return [(1,)]\n", " result = []\n", " for first in range(1, n + 1):\n", " for rest in integer_compositions(n - first):\n", " result.append((first,) + rest)\n", " return result\n", "\n", "\n", "class ConvPath(nn.Module):\n", " def __init__(self, composition, embed_dim, hidden_dim):\n", " super().__init__()\n", " self.composition = composition\n", " self.hidden_dim = hidden_dim\n", " self.steps = nn.ModuleList()\n", " current_dim = embed_dim\n", " for k in composition:\n", " self.steps.append(nn.ModuleDict({\n", " \"proj\": nn.Linear(current_dim, hidden_dim),\n", " \"mix\": nn.Linear(hidden_dim, hidden_dim),\n", " \"norm\": nn.LayerNorm(hidden_dim),\n", " }))\n", " self.steps[-1].k_value = k\n", " current_dim = hidden_dim\n", " self.out = nn.Linear(hidden_dim, embed_dim)\n", "\n", " def forward(self, x):\n", " B = x.shape[0]\n", " h = x\n", " for step in self.steps:\n", " k = step.k_value\n", " h = F.gelu(step[\"proj\"](h))\n", " if k > 1 and self.hidden_dim >= k and self.hidden_dim % k == 0:\n", " g = h.view(B, self.hidden_dim // k, k)\n", " g = g * torch.softmax(g, dim=-1)\n", " h = g.view(B, self.hidden_dim)\n", " h = F.gelu(step[\"mix\"](h))\n", " h = step[\"norm\"](h)\n", " return self.out(h)\n", "\n", "\n", "class CompConv5d(nn.Module):\n", " def __init__(self, embed_dim, hidden_dim):\n", " super().__init__()\n", " self.compositions = integer_compositions(5) # 16 paths\n", " self.paths = nn.ModuleList([\n", " ConvPath(c, embed_dim, hidden_dim) for c in self.compositions])\n", " self.weights = nn.Parameter(torch.ones(16) / 16)\n", " self.fusion = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim), nn.GELU(), nn.LayerNorm(embed_dim))\n", "\n", " def forward(self, x):\n", " outs = torch.stack([p(x) for p in self.paths], dim=1)\n", " w = F.softmax(self.weights, dim=0)\n", " return self.fusion((outs * w.view(1, -1, 1)).sum(1))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIVE-ANCHOR MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class FiveAnchorModel(nn.Module):\n", " def __init__(self, embed_dim=768, hidden_dim=256, n_anchors=5):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.conv5 = CompConv5d(embed_dim, hidden_dim)\n", "\n", " # Combine all 5 anchors + structural signal\n", " self.combine = nn.Sequential(\n", " nn.Linear(embed_dim * (n_anchors + 1), embed_dim),\n", " nn.GELU(), nn.LayerNorm(embed_dim))\n", " self.output = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim),\n", " nn.GELU(), nn.Linear(embed_dim, embed_dim))\n", "\n", " def forward(self, anchors):\n", " \"\"\"\n", " anchors: list of 5 tensors, each (B, 768)\n", " Returns: (B, 768) unified embedding\n", " \"\"\"\n", " # Structural signal: sum of all pairwise differences\n", " # This captures the full 5-way structural relationship\n", " structural = torch.zeros_like(anchors[0])\n", " for i in range(self.n_anchors):\n", " for j in range(i+1, self.n_anchors):\n", " structural = structural + (anchors[i] - anchors[j])\n", "\n", " # Decompose through conv5d\n", " decomposed = self.conv5(structural)\n", "\n", " # Mean of all anchors\n", " mean_anchor = sum(anchors) / self.n_anchors\n", "\n", " # Combine: decomposed + all anchors\n", " combined = self.combine(torch.cat([decomposed] + anchors, dim=-1))\n", "\n", " return F.normalize(self.output(combined + mean_anchor), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOSSES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.20, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "def svd_align(a, b):\n", " A = F.normalize(a.float(), dim=-1)\n", " B_e = F.normalize(b.float(), dim=-1)\n", " A = A - A.mean(0, keepdim=True)\n", " B_e = B_e - B_e.mean(0, keepdim=True)\n", " N, D = A.shape\n", " try:\n", " S = torch.linalg.svdvals(A @ B_e.T) if N < D else torch.linalg.svdvals(A.T @ B_e)\n", " except Exception:\n", " return torch.tensor(0.0, device=a.device, requires_grad=True)\n", " return 1.0 - S.sum() / (math.sqrt(N) * D)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train():\n", " # ── Extract ──\n", " embeds, captions = extract_all()\n", "\n", " # ── Align all to first model's space ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PROCRUSTES ALIGNMENT (all → bert space)\")\n", " print(f\"{'='*65}\")\n", "\n", " ref_name = MODELS[0][1] # bert\n", " aligned = {}\n", " align_info = {}\n", "\n", " for _, short in MODELS:\n", " if short == ref_name:\n", " aligned[short] = embeds[short].float()\n", " align_info[short] = {\"cos_before\": 1.0, \"cos_after\": 1.0}\n", " print(f\" {short:10s}: reference (identity)\")\n", " continue\n", "\n", " info = procrustes_align(embeds[short], embeds[ref_name])\n", " aligned[short] = apply_align(embeds[short], info)\n", " align_info[short] = info\n", " print(f\" {short:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}\")\n", "\n", " # Pairwise cosines after alignment\n", " print(f\"\\n Pairwise cosines (post-alignment):\")\n", " names = [s for _, s in MODELS]\n", " for i in range(len(names)):\n", " for j in range(i+1, len(names)):\n", " cos = F.cosine_similarity(\n", " aligned[names[i]][:5000], aligned[names[j]][:5000], dim=-1\n", " ).mean().item()\n", " print(f\" {names[i]:8s} ↔ {names[j]:8s}: {cos:.4f}\")\n", "\n", " # Split\n", " n_val = 2000\n", " n_train = CFG.n_samples - n_val\n", " train_data = {k: v[:n_train].to(DEVICE) for k, v in aligned.items()}\n", " val_data = {k: v[n_train:].to(DEVICE) for k, v in aligned.items()}\n", " anchor_names = [s for _, s in MODELS]\n", " print(f\"\\n Train: {n_train}, Val: {n_val}\")\n", "\n", " # ── Model ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"MODEL: FiveAnchorModel + CompConv5d\")\n", " print(f\"{'='*65}\")\n", "\n", " model = FiveAnchorModel(768, CFG.hidden_dim, 5).to(DEVICE)\n", " n_params = sum(p.numel() for p in model.parameters())\n", " print(f\" Parameters: {n_params:,}\")\n", " print(f\" Conv5d paths: {len(model.conv5.compositions)} (2^4 = 16)\")\n", "\n", " # ── Train ──\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({CFG.epochs} epochs)\")\n", " print(f\" 5 InfoNCE + 5 SVD + 1 CV = 11 losses\")\n", " print(f\"{'='*65}\")\n", "\n", " optimizer = torch.optim.AdamW(model.parameters(), lr=CFG.lr,\n", " weight_decay=CFG.weight_decay)\n", " n_batches = n_train // CFG.train_batch\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=n_batches * CFG.epochs, eta_min=1e-6)\n", "\n", " all_metrics = {\"epochs\": [], \"path_weights\": [], \"alignment\": align_info}\n", "\n", " for epoch in range(CFG.epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " accs = {n: 0 for n in anchor_names}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, CFG.train_batch):\n", " idx = perm[i:i+CFG.train_batch]\n", " if len(idx) < 8: continue\n", "\n", " batch_anchors = [train_data[name][idx] for name in anchor_names]\n", " emb = model(batch_anchors)\n", "\n", " # 5 InfoNCE losses + 5 SVD losses\n", " loss = torch.tensor(0.0, device=DEVICE)\n", " for k, name in enumerate(anchor_names):\n", " l_nce, acc = infonce(emb, batch_anchors[k])\n", " l_svd = svd_align(emb, batch_anchors[k])\n", " loss = loss + CFG.nce_weight * l_nce + CFG.svd_weight * l_svd\n", " accs[name] += acc\n", "\n", " # CV loss\n", " l_cv = cv_loss(emb)\n", " loss = loss + CFG.cv_weight * l_cv\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), CFG.grad_clip)\n", " optimizer.step()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " val_anchors = [val_data[name] for name in anchor_names]\n", " val_emb = model(val_anchors)\n", " val_accs = {}\n", " for k, name in enumerate(anchor_names):\n", " _, va = infonce(val_emb, val_anchors[k])\n", " val_accs[name] = va\n", " val_cv = cv_metric(val_emb)\n", "\n", " # Path weights\n", " pw = F.softmax(model.conv5.weights, dim=0).detach().cpu().tolist()\n", "\n", " # Top 3 paths\n", " sorted_paths = sorted(zip(model.conv5.compositions, pw), key=lambda x: -x[1])\n", " top3 = \" \".join(f\"{str(c)}={w:.4f}\" for c, w in sorted_paths[:3])\n", "\n", " # Acc summary\n", " acc_str = \"/\".join(f\"{accs[name]/d:.3f}\" for name in anchor_names)\n", " vacc_str = \"/\".join(f\"{val_accs[name]:.3f}\" for name in anchor_names)\n", "\n", " summary = {\n", " \"epoch\": epoch + 1, \"loss\": total_loss / d,\n", " \"train_accs\": {n: accs[n]/d for n in anchor_names},\n", " \"val_accs\": val_accs, \"val_cv\": val_cv,\n", " }\n", " all_metrics[\"epochs\"].append(summary)\n", " all_metrics[\"path_weights\"].append(pw)\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} \"\n", " f\"acc={acc_str} val={vacc_str} cv={val_cv:.3f} top: {top3}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # FINAL ANALYSIS\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"FINAL PATH WEIGHT ANALYSIS (16 paths)\")\n", " print(f\"{'='*65}\")\n", "\n", " pw = F.softmax(model.conv5.weights, dim=0).detach().cpu()\n", " labels = {1: \"indep\", 2: \"pair\", 3: \"3-way\", 4: \"4-way\", 5: \"5-way\"}\n", " for comp, w in sorted(zip(model.conv5.compositions, pw.tolist()), key=lambda x: -x[1]):\n", " bar = \"█\" * int(w * 80)\n", " interp = \" → \".join(labels.get(k, f\"{k}\") for k in comp)\n", " print(f\" {str(comp):<25} {w:.4f} {bar} ({interp})\")\n", "\n", " # Weight spread\n", " pw_arr = pw.numpy()\n", " print(f\"\\n Weight spread: min={pw_arr.min():.4f} max={pw_arr.max():.4f} \"\n", " f\"std={pw_arr.std():.6f} range={pw_arr.max()-pw_arr.min():.6f}\")\n", " print(f\" Uniform would be: {1/16:.4f} = 0.0625\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " print(f\"\\n{'='*65}\")\n", " print(\"CONSENSUS GEOMETRY\")\n", " print(f\"{'='*65}\")\n", "\n", " model.eval()\n", " with torch.no_grad():\n", " val_anchors = [val_data[name] for name in anchor_names]\n", " val_emb = model(val_anchors).cpu()\n", "\n", " # Cosine to each anchor\n", " for name in anchor_names:\n", " cos = F.cosine_similarity(\n", " val_emb, val_data[name].cpu(), dim=-1).mean().item()\n", " print(f\" cos(output, {name:10s}): {cos:.4f}\")\n", "\n", " # Cosine to geometric centroid\n", " centroid = F.normalize(sum(val_data[n].cpu() for n in anchor_names) / 5, dim=-1)\n", " cos_cent = F.cosine_similarity(val_emb, centroid, dim=-1).mean().item()\n", " print(f\" cos(output, centroid): {cos_cent:.4f}\")\n", "\n", " # CV comparison\n", " out_cv = cv_metric(val_emb[:1000])\n", " print(f\"\\n CV output: {out_cv:.4f}\")\n", " for name in anchor_names:\n", " acv = cv_metric(val_data[name].cpu()[:1000])\n", " print(f\" CV {name:10s}: {acv:.4f}\")\n", "\n", " # Pairwise cosines between output and each anchor\n", " print(f\"\\n Equidistance check (should be ~equal):\")\n", " cosines = []\n", " for name in anchor_names:\n", " cos = F.cosine_similarity(\n", " val_emb, val_data[name].cpu(), dim=-1).mean().item()\n", " cosines.append(cos)\n", " print(f\" Range: {max(cosines)-min(cosines):.6f}\")\n", " print(f\" Std: {np.std(cosines):.6f}\")\n", "\n", " # Save\n", " os.makedirs(CFG.cache_dir, exist_ok=True)\n", " with open(os.path.join(CFG.cache_dir, \"five_results.json\"), \"w\") as f:\n", " json.dump(all_metrics, f, indent=2, default=str)\n", " torch.save(model.state_dict(), os.path.join(CFG.cache_dir, \"five_model.pt\"))\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ 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"dda522f33a23466db417679e65673e24", "aeafa57dec3442ccb07287952e31bf16", "bd51f8731dce43a0a73da9133037a9f7", "9c98f555b91d4edd9e1e84043e60edc7", "0e381b6c796443a0a5734254bbe09ea5", "95c3a67d20a74da58e619915e17d25ba", "ee5c30553b3e48588295b90295d7943a", "23f17bc680d34ad194a26b8b93693e1b", "d47da607c91640ed93859e5be6137d01", "77727da895e64e33afb03b21abf632a9", "f9d1285d6c9f47d4a5586a4002b3461f", "8b8b9f5e4dc741da8fcdaa667fe4cffa", "2e7d00e6737d419daa31f38427df40d2", "40dc7545a0b547a396d666457057cc34", "760719f9e92349ca9360e900e589271e", "925232a874d840fb8ae504c5cdcbec2f", "6bce40c76023457281f6838b3ba74f69", "54538f8399184581837b414057445b49", "2ab7eee807c746ffa44fdff3fec9cd2f", "a81bad4243984c99a1f2fbe0355f72c3", "f6585ffef775480c867f8ac095bdc292", "c70e8217531d4778a4d7341a8604e514", "afe1af71c4dd44929c2a1b2a8d02f6e9", "aede4d97d7ef4e899ec63d29b8fbfe9c", "2a4db79fbbaa4e7b885c0c8dfbf3a9f6", "00f59dabbd1a4377ba0b0bbebd7523a6", "49342ca5408f4ba1985b64e307802b99", "68988518c92545c0a2fa1900e6ad30e1", "2ccded8d8b0a4317947c3f04813bdcc0", "345dc059805148d5ae130956fe755da8", "b9767ace4e39498188862d285e1c7234", "346ce9b77d454356b569ec3383f1492f", "4c3d20d7ede34cb0934bc1ca0b7ec4bd", "18449ba42d6f4997aecdcf7ccc0253c7", "e3c98c90f79f4917afbcb712e2408839", "831f5133f327465da02e6ac03a77e03e", "9baf758c7b324cba91f3af4d9107d944", "b639b1a63b59473daad7deced5c3d60c", "933a7bd9df2c43a3b71ecd02d897aa9f", "23593ce9f71d4424b7e089d78111baab", "25b604ffaf9d49878f4326ffcc90fc11", "0edd02992610457397183dcfe4969f01" ] }, "id": "5kguAb7TQZ_L", "outputId": "aa99fbeb-3632-4cc7-b0d3-5b1bbf1c9a03" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "FIVE BERTS: PENTACHORON CONSENSUS\n", "=================================================================\n", " Device: cuda\n", " Models: ['bert', 'modern', 'roberta', 'albert', 'distil']\n", "\n", " Loading captions (n=20000)...\n", " Got 20000 captions\n", "\n", " Extracting: bert (google-bert/bert-base-uncased)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 min_len:\n", " captions.append(cap)\n", " if len(captions) >= n:\n", " break\n", " print(f\" Got {len(captions)} captions\")\n", " return captions\n", "\n", "\n", "@torch.no_grad()\n", "def extract_one(model_name, short_name, captions, max_len, batch_size):\n", " from transformers import AutoModel, AutoTokenizer\n", " print(f\"\\n Extracting: {short_name} ({model_name})...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " dim = model.config.hidden_size\n", " print(f\" dim={dim}, {sum(p.numel() for p in model.parameters()):,} params\")\n", "\n", " all_emb = []\n", " for i in tqdm(range(0, len(captions), batch_size), desc=f\" {short_name}\"):\n", " batch = captions[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " # Mean pool\n", " hs = out.last_hidden_state\n", " mask = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (hs * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", "\n", " emb = torch.cat(all_emb)\n", " print(f\" Shape: {emb.shape}\")\n", " del model\n", " torch.cuda.empty_cache()\n", " return emb\n", "\n", "\n", "def extract_all():\n", " os.makedirs(CFG.cache_dir, exist_ok=True)\n", " caps_path = os.path.join(CFG.cache_dir, \"captions.json\")\n", "\n", " # Check cache\n", " all_cached = all(\n", " os.path.exists(os.path.join(CFG.cache_dir, f\"{short}.pt\"))\n", " for _, short in MODELS)\n", "\n", " if all_cached:\n", " print(\"\\n Loading cached embeddings...\")\n", " embeds = {}\n", " for _, short in MODELS:\n", " embeds[short] = torch.load(\n", " os.path.join(CFG.cache_dir, f\"{short}.pt\"), weights_only=True)\n", " print(f\" {short}: {embeds[short].shape}\")\n", " with open(caps_path) as f:\n", " captions = json.load(f)\n", " return embeds, captions\n", "\n", " captions = load_captions(CFG.n_samples, CFG.min_caption_len)\n", "\n", " embeds = {}\n", " for model_name, short in MODELS:\n", " emb = extract_one(model_name, short, captions,\n", " CFG.max_len, CFG.batch_size)\n", " # Handle dimension mismatch — ALBERT-base-v2 is 768 but some\n", " # models might differ. Pad/truncate to 768.\n", " if emb.shape[1] != 768:\n", " print(f\" Adjusting {short} from {emb.shape[1]} to 768\")\n", " if emb.shape[1] < 768:\n", " emb = F.pad(emb, (0, 768 - emb.shape[1]))\n", " else:\n", " emb = emb[:, :768]\n", " embeds[short] = emb\n", " torch.save(emb, os.path.join(CFG.cache_dir, f\"{short}.pt\"))\n", "\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", "\n", " return embeds, captions\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES ALIGNMENT (all to first model's space)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " \"\"\"Covariance matrix inverse square root for whitening.\"\"\"\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " \"\"\"Whitened Procrustes: normalize variance before rotating.\"\"\"\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float()\n", " T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True)\n", " t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean\n", " Tc = T - t_mean\n", "\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", "\n", " N_s, D = Sc.shape\n", "\n", " # Whiten both distributions — every dimension contributes equally\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", "\n", " Sc_w = Sc @ s_whiten\n", " Tc_w = Tc @ t_whiten\n", "\n", " # Normalize after whitening\n", " Sc_w = F.normalize(Sc_w, dim=-1)\n", " Tc_w = F.normalize(Tc_w, dim=-1)\n", "\n", " # SVD rotation on whitened coordinates\n", " try:\n", " cross = Tc_w.T @ Sc_w\n", " U, _, Vt = torch.linalg.svd(cross, full_matrices=False)\n", " R = U @ Vt\n", " except Exception:\n", " R = torch.eye(D)\n", "\n", " # Compute unwhitener for target (to map back from whitened to target space)\n", " t_unwhiten = torch.linalg.pinv(t_whiten)\n", "\n", " Sc_aligned = Sc_w @ R.T\n", " cos_after = F.cosine_similarity(Sc_aligned, Tc_w, dim=-1).mean().item()\n", "\n", " return {\n", " \"rotation\": R,\n", " \"source_mean\": s_mean.squeeze(0),\n", " \"target_mean\": t_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_whitener\": t_whiten,\n", " \"target_unwhitener\": t_unwhiten,\n", " \"cos_before\": cos_before,\n", " \"cos_after\": cos_after,\n", " }\n", "\n", "\n", "def apply_align(emb, alignment):\n", " \"\"\"Apply whitened Procrustes: center → whiten → rotate → unwhiten.\"\"\"\n", " x = emb.float() - alignment[\"source_mean\"]\n", " x = x @ alignment[\"source_whitener\"]\n", " x = x @ alignment[\"rotation\"].T\n", " x = x @ alignment[\"target_unwhitener\"]\n", " return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPOSITIONAL CONV5D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def integer_compositions(n):\n", " if n == 0: return [()]\n", " if n == 1: return [(1,)]\n", " result = []\n", " for first in range(1, n + 1):\n", " for rest in integer_compositions(n - first):\n", " result.append((first,) + rest)\n", " return result\n", "\n", "\n", "class ConvPath(nn.Module):\n", " def __init__(self, composition, embed_dim, hidden_dim):\n", " super().__init__()\n", " self.composition = composition\n", " self.hidden_dim = hidden_dim\n", " self.steps = nn.ModuleList()\n", " current_dim = embed_dim\n", " for k in composition:\n", " self.steps.append(nn.ModuleDict({\n", " \"proj\": nn.Linear(current_dim, hidden_dim),\n", " \"mix\": nn.Linear(hidden_dim, hidden_dim),\n", " \"norm\": nn.LayerNorm(hidden_dim),\n", " }))\n", " self.steps[-1].k_value = k\n", " current_dim = hidden_dim\n", " self.out = nn.Linear(hidden_dim, embed_dim)\n", "\n", " def forward(self, x):\n", " B = x.shape[0]\n", " h = x\n", " for step in self.steps:\n", " k = step.k_value\n", " h = F.gelu(step[\"proj\"](h))\n", " if k > 1 and self.hidden_dim >= k and self.hidden_dim % k == 0:\n", " g = h.view(B, self.hidden_dim // k, k)\n", " g = g * torch.softmax(g, dim=-1)\n", " h = g.view(B, self.hidden_dim)\n", " h = F.gelu(step[\"mix\"](h))\n", " h = step[\"norm\"](h)\n", " return self.out(h)\n", "\n", "\n", "class CompConv5d(nn.Module):\n", " def __init__(self, embed_dim, hidden_dim):\n", " super().__init__()\n", " self.compositions = integer_compositions(5) # 16 paths\n", " self.paths = nn.ModuleList([\n", " ConvPath(c, embed_dim, hidden_dim) for c in self.compositions])\n", " self.weights = nn.Parameter(torch.ones(16) / 16)\n", " self.fusion = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim), nn.GELU(), nn.LayerNorm(embed_dim))\n", "\n", " def forward(self, x):\n", " outs = torch.stack([p(x) for p in self.paths], dim=1)\n", " w = F.softmax(self.weights, dim=0)\n", " return self.fusion((outs * w.view(1, -1, 1)).sum(1))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIVE-ANCHOR MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class FiveAnchorModel(nn.Module):\n", " def __init__(self, embed_dim=768, hidden_dim=256, n_anchors=5):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.conv5 = CompConv5d(embed_dim, hidden_dim)\n", "\n", " # Combine all 5 anchors + structural signal\n", " self.combine = nn.Sequential(\n", " nn.Linear(embed_dim * (n_anchors + 1), embed_dim),\n", " nn.GELU(), nn.LayerNorm(embed_dim))\n", " self.output = nn.Sequential(\n", " nn.Linear(embed_dim, embed_dim),\n", " nn.GELU(), nn.Linear(embed_dim, embed_dim))\n", "\n", " def forward(self, anchors):\n", " \"\"\"\n", " anchors: list of 5 tensors, each (B, 768)\n", " Returns: (B, 768) unified embedding\n", " \"\"\"\n", " # Structural signal: sum of all pairwise differences\n", " # This captures the full 5-way structural relationship\n", " structural = torch.zeros_like(anchors[0])\n", " for i in range(self.n_anchors):\n", " for j in range(i+1, self.n_anchors):\n", " structural = structural + (anchors[i] - anchors[j])\n", "\n", " # Decompose through conv5d\n", " decomposed = self.conv5(structural)\n", "\n", " # Mean of all anchors\n", " mean_anchor = sum(anchors) / self.n_anchors\n", "\n", " # Combine: decomposed + all anchors\n", " combined = self.combine(torch.cat([decomposed] + anchors, dim=-1))\n", "\n", " return F.normalize(self.output(combined + mean_anchor), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOSSES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.20, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "def svd_align(a, b):\n", " A = F.normalize(a.float(), dim=-1)\n", " B_e = F.normalize(b.float(), dim=-1)\n", " A = A - A.mean(0, keepdim=True)\n", " B_e = B_e - B_e.mean(0, keepdim=True)\n", " N, D = A.shape\n", " try:\n", " S = torch.linalg.svdvals(A @ B_e.T) if N < D else torch.linalg.svdvals(A.T @ B_e)\n", " except Exception:\n", " return torch.tensor(0.0, device=a.device, requires_grad=True)\n", " return 1.0 - S.sum() / (math.sqrt(N) * D)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train():\n", " # ── Seed everything ──\n", " torch.manual_seed(CFG.seed)\n", " torch.cuda.manual_seed_all(CFG.seed)\n", " np.random.seed(CFG.seed)\n", " print(f\"\\n Seed: {CFG.seed}\")\n", "\n", " # ── Extract ──\n", " embeds, captions = extract_all()\n", "\n", " # ── Align all to first model's space ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PROCRUSTES ALIGNMENT (all → bert space)\")\n", " print(f\"{'='*65}\")\n", "\n", " ref_name = MODELS[0][1] # bert\n", " aligned = {}\n", " align_info = {}\n", "\n", " for _, short in MODELS:\n", " # All models go through whitened Procrustes — including reference\n", " # Self-alignment whitens the reference into the same normalized space\n", " info = procrustes_align(embeds[short], embeds[ref_name])\n", " aligned[short] = apply_align(embeds[short], info)\n", " align_info[short] = info\n", " label = \" (reference)\" if short == ref_name else \"\"\n", " print(f\" {short:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}{label}\")\n", "\n", " # Pairwise cosines after alignment\n", " print(f\"\\n Pairwise cosines (post-alignment):\")\n", " names = [s for _, s in MODELS]\n", " for i in range(len(names)):\n", " for j in range(i+1, len(names)):\n", " cos = F.cosine_similarity(\n", " aligned[names[i]][:5000], aligned[names[j]][:5000], dim=-1\n", " ).mean().item()\n", " print(f\" {names[i]:8s} ↔ {names[j]:8s}: {cos:.4f}\")\n", "\n", " # Split\n", " n_val = 2000\n", " n_train = CFG.n_samples - n_val\n", " train_data = {k: v[:n_train].to(DEVICE) for k, v in aligned.items()}\n", " val_data = {k: v[n_train:].to(DEVICE) for k, v in aligned.items()}\n", " anchor_names = [s for _, s in MODELS]\n", " print(f\"\\n Train: {n_train}, Val: {n_val}\")\n", "\n", " # ── Model ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"MODEL: FiveAnchorModel + CompConv5d\")\n", " print(f\"{'='*65}\")\n", "\n", " model = FiveAnchorModel(768, CFG.hidden_dim, 5).to(DEVICE)\n", " n_params = sum(p.numel() for p in model.parameters())\n", " print(f\" Parameters: {n_params:,}\")\n", " print(f\" Conv5d paths: {len(model.conv5.compositions)} (2^4 = 16)\")\n", "\n", " # ── Train ──\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({CFG.epochs} epochs)\")\n", " print(f\" 5 InfoNCE + 5 SVD + 1 CV = 11 losses\")\n", " print(f\"{'='*65}\")\n", "\n", " optimizer = torch.optim.AdamW(model.parameters(), lr=CFG.lr,\n", " weight_decay=CFG.weight_decay)\n", " n_batches = n_train // CFG.train_batch\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=n_batches * CFG.epochs, eta_min=1e-6)\n", "\n", " all_metrics = {\"seed\": CFG.seed, \"epochs\": [], \"path_weights\": [], \"alignment\": align_info}\n", "\n", " for epoch in range(CFG.epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " accs = {n: 0 for n in anchor_names}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, CFG.train_batch):\n", " idx = perm[i:i+CFG.train_batch]\n", " if len(idx) < 8: continue\n", "\n", " batch_anchors = [train_data[name][idx] for name in anchor_names]\n", " emb = model(batch_anchors)\n", "\n", " # 5 InfoNCE losses + 5 SVD losses\n", " loss = torch.tensor(0.0, device=DEVICE)\n", " for k, name in enumerate(anchor_names):\n", " l_nce, acc = infonce(emb, batch_anchors[k])\n", " l_svd = svd_align(emb, batch_anchors[k])\n", " loss = loss + CFG.nce_weight * l_nce + CFG.svd_weight * l_svd\n", " accs[name] += acc\n", "\n", " # CV loss\n", " l_cv = cv_loss(emb)\n", " loss = loss + CFG.cv_weight * l_cv\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), CFG.grad_clip)\n", " optimizer.step()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " val_anchors = [val_data[name] for name in anchor_names]\n", " val_emb = model(val_anchors)\n", " val_accs = {}\n", " for k, name in enumerate(anchor_names):\n", " _, va = infonce(val_emb, val_anchors[k])\n", " val_accs[name] = va\n", " val_cv = cv_metric(val_emb)\n", "\n", " # Path weights\n", " pw = F.softmax(model.conv5.weights, dim=0).detach().cpu().tolist()\n", "\n", " # Top 3 paths\n", " sorted_paths = sorted(zip(model.conv5.compositions, pw), key=lambda x: -x[1])\n", " top3 = \" \".join(f\"{str(c)}={w:.4f}\" for c, w in sorted_paths[:3])\n", "\n", " # Acc summary\n", " acc_str = \"/\".join(f\"{accs[name]/d:.3f}\" for name in anchor_names)\n", " vacc_str = \"/\".join(f\"{val_accs[name]:.3f}\" for name in anchor_names)\n", "\n", " summary = {\n", " \"epoch\": epoch + 1, \"loss\": total_loss / d,\n", " \"train_accs\": {n: accs[n]/d for n in anchor_names},\n", " \"val_accs\": val_accs, \"val_cv\": val_cv,\n", " }\n", " all_metrics[\"epochs\"].append(summary)\n", " all_metrics[\"path_weights\"].append(pw)\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} \"\n", " f\"acc={acc_str} val={vacc_str} cv={val_cv:.3f} top: {top3}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # FINAL ANALYSIS\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"FINAL PATH WEIGHT ANALYSIS (16 paths)\")\n", " print(f\"{'='*65}\")\n", "\n", " pw = F.softmax(model.conv5.weights, dim=0).detach().cpu()\n", " labels = {1: \"indep\", 2: \"pair\", 3: \"3-way\", 4: \"4-way\", 5: \"5-way\"}\n", " for comp, w in sorted(zip(model.conv5.compositions, pw.tolist()), key=lambda x: -x[1]):\n", " bar = \"█\" * int(w * 80)\n", " interp = \" → \".join(labels.get(k, f\"{k}\") for k in comp)\n", " print(f\" {str(comp):<25} {w:.4f} {bar} ({interp})\")\n", "\n", " # Weight spread\n", " pw_arr = pw.numpy()\n", " print(f\"\\n Weight spread: min={pw_arr.min():.4f} max={pw_arr.max():.4f} \"\n", " f\"std={pw_arr.std():.6f} range={pw_arr.max()-pw_arr.min():.6f}\")\n", " print(f\" Uniform would be: {1/16:.4f} = 0.0625\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " print(f\"\\n{'='*65}\")\n", " print(\"CONSENSUS GEOMETRY\")\n", " print(f\"{'='*65}\")\n", "\n", " model.eval()\n", " with torch.no_grad():\n", " val_anchors = [val_data[name] for name in anchor_names]\n", " val_emb = model(val_anchors).cpu()\n", "\n", " # Cosine to each anchor\n", " for name in anchor_names:\n", " cos = F.cosine_similarity(\n", " val_emb, val_data[name].cpu(), dim=-1).mean().item()\n", " print(f\" cos(output, {name:10s}): {cos:.4f}\")\n", "\n", " # Cosine to geometric centroid\n", " centroid = F.normalize(sum(val_data[n].cpu() for n in anchor_names) / 5, dim=-1)\n", " cos_cent = F.cosine_similarity(val_emb, centroid, dim=-1).mean().item()\n", " print(f\" cos(output, centroid): {cos_cent:.4f}\")\n", "\n", " # CV comparison\n", " out_cv = cv_metric(val_emb[:1000])\n", " print(f\"\\n CV output: {out_cv:.4f}\")\n", " for name in anchor_names:\n", " acv = cv_metric(val_data[name].cpu()[:1000])\n", " print(f\" CV {name:10s}: {acv:.4f}\")\n", "\n", " # Pairwise cosines between output and each anchor\n", " print(f\"\\n Equidistance check (should be ~equal):\")\n", " cosines = []\n", " for name in anchor_names:\n", " cos = F.cosine_similarity(\n", " val_emb, val_data[name].cpu(), dim=-1).mean().item()\n", " cosines.append(cos)\n", " print(f\" Range: {max(cosines)-min(cosines):.6f}\")\n", " print(f\" Std: {np.std(cosines):.6f}\")\n", "\n", " # Save\n", " os.makedirs(CFG.cache_dir, exist_ok=True)\n", " with open(os.path.join(CFG.cache_dir, f\"five_results_seed{CFG.seed}.json\"), \"w\") as f:\n", " json.dump(all_metrics, f, indent=2, default=str)\n", " torch.save(model.state_dict(), os.path.join(CFG.cache_dir, f\"five_model_seed{CFG.seed}.pt\"))\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " # Default: run 3 seeds\n", " seeds = [42, 43, 44, 12341, 12323, 8675309]\n", "\n", " for seed in seeds:\n", " CFG.seed = seed\n", " train()\n", " print(f\"\\n{'#'*65}\")\n", " print(f\"# SEED {seed} COMPLETE\")\n", " print(f\"{'#'*65}\\n\")\n", " torch.cuda.empty_cache()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tzbRxCfQTbqQ", "outputId": "68cd9008-a3c8-4c85-d11f-a5271d056499" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "FIVE BERTS: PENTACHORON CONSENSUS\n", "=================================================================\n", " Device: cuda\n", " Models: ['bert', 'modern', 'roberta', 'albert', 'distil']\n", "\n", " Seed: 42\n", "\n", " Loading cached embeddings...\n", " bert: torch.Size([20000, 768])\n", " modern: torch.Size([20000, 768])\n", " roberta: torch.Size([20000, 768])\n", " albert: torch.Size([20000, 768])\n", " distil: torch.Size([20000, 768])\n", "\n", "=================================================================\n", "PROCRUSTES ALIGNMENT (all → bert space)\n", "=================================================================\n", " bert : cos 1.0000 → 1.0000 (reference)\n", " modern : cos -0.0025 → 0.4849\n", " roberta : cos -0.0037 → 0.5138\n", " albert : cos -0.0004 → 0.4888\n", " distil : cos 0.8567 → 0.6557\n", "\n", " Pairwise cosines (post-alignment):\n", " bert ↔ modern : 0.8357\n", " bert ↔ roberta : 0.8685\n", " bert ↔ albert : 0.8413\n", " bert ↔ distil : 0.9314\n", " modern ↔ roberta : 0.8040\n", " modern ↔ albert : 0.7777\n", " modern ↔ distil : 0.8224\n", " roberta ↔ albert : 0.8039\n", " roberta ↔ distil : 0.8528\n", " albert ↔ distil : 0.8269\n", "\n", " Train: 18000, Val: 2000\n", "\n", "=================================================================\n", "MODEL: FiveAnchorModel + CompConv5d\n", "=================================================================\n", " Parameters: 16,910,352\n", " Conv5d paths: 16 (2^4 = 16)\n", "\n", "=================================================================\n", "TRAINING (20 epochs)\n", " 5 InfoNCE + 5 SVD + 1 CV = 11 losses\n", "=================================================================\n", " E 1: 4s loss=3.4940 acc=0.927/0.912/0.920/0.920/0.924 val=1.000/1.000/1.000/0.999/1.000 cv=0.119 top: (2, 1, 2)=0.0628 (2, 1, 1, 1)=0.0628 (4, 1)=0.0627\n", " E 2: 4s loss=0.5318 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.105 top: (2, 1, 2)=0.0628 (2, 1, 1, 1)=0.0628 (4, 1)=0.0628\n", " E 3: 4s loss=0.4549 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.105 top: (2, 1, 2)=0.0629 (2, 1, 1, 1)=0.0628 (4, 1)=0.0628\n", " E 4: 4s loss=0.4216 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.104 top: (2, 1, 2)=0.0629 (4, 1)=0.0628 (2, 1, 1, 1)=0.0628\n", " E 5: 4s loss=0.4071 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.098 top: (2, 1, 2)=0.0629 (4, 1)=0.0628 (2, 1, 1, 1)=0.0628\n", " E 6: 4s loss=0.3956 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.100 top: (2, 1, 2)=0.0629 (4, 1)=0.0628 (2, 1, 1, 1)=0.0627\n", " E 7: 4s loss=0.3864 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.097 top: (2, 1, 2)=0.0628 (4, 1)=0.0628 (2, 1, 1, 1)=0.0627\n", " E 8: 4s loss=0.3797 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.092 top: (2, 1, 2)=0.0628 (4, 1)=0.0628 (2, 1, 1, 1)=0.0627\n", " E 9: 4s loss=0.3735 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.096 top: (2, 1, 2)=0.0628 (4, 1)=0.0628 (2, 1, 1, 1)=0.0627\n", " E10: 4s loss=0.3690 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.094 top: (2, 1, 2)=0.0628 (4, 1)=0.0628 (2, 1, 1, 1)=0.0627\n", " E11: 4s loss=0.3653 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.090 top: (2, 1, 2)=0.0628 (4, 1)=0.0628 (2, 1, 1, 1)=0.0627\n", " E12: 4s loss=0.3643 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (2, 1, 2)=0.0628 (4, 1)=0.0628 (2, 1, 1, 1)=0.0627\n", " E13: 4s loss=0.3601 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.090 top: (2, 1, 2)=0.0628 (4, 1)=0.0627 (2, 2, 1)=0.0626\n", " E14: 4s loss=0.3583 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.089 top: (2, 1, 2)=0.0628 (4, 1)=0.0627 (2, 2, 1)=0.0626\n", " E15: 4s loss=0.3545 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.081 top: (2, 1, 2)=0.0627 (4, 1)=0.0627 (2, 2, 1)=0.0626\n", " E16: 4s loss=0.3527 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.078 top: (2, 1, 2)=0.0627 (4, 1)=0.0627 (2, 2, 1)=0.0626\n", " E17: 4s loss=0.3519 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.079 top: (4, 1)=0.0627 (2, 1, 2)=0.0627 (2, 2, 1)=0.0626\n", " E18: 4s loss=0.3522 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.085 top: (4, 1)=0.0627 (2, 1, 2)=0.0627 (2, 2, 1)=0.0626\n", " E19: 4s loss=0.3505 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.072 top: (4, 1)=0.0627 (2, 1, 2)=0.0627 (2, 2, 1)=0.0626\n", " E20: 4s loss=0.3505 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.078 top: (4, 1)=0.0627 (2, 1, 2)=0.0627 (2, 2, 1)=0.0626\n", "\n", "=================================================================\n", "FINAL PATH WEIGHT ANALYSIS (16 paths)\n", "=================================================================\n", " (4, 1) 0.0627 █████ (4-way → indep)\n", " (2, 1, 2) 0.0627 █████ (pair → indep → pair)\n", " (2, 2, 1) 0.0626 █████ (pair → pair → indep)\n", " (2, 1, 1, 1) 0.0626 █████ (pair → indep → indep → indep)\n", " (1, 2, 2) 0.0626 █████ (indep → pair → pair)\n", " (2, 3) 0.0626 █████ (pair → 3-way)\n", " (1, 1, 3) 0.0626 █████ (indep → indep → 3-way)\n", " (1, 2, 1, 1) 0.0625 █████ (indep → pair → indep → indep)\n", " (1, 1, 2, 1) 0.0625 ████ (indep → indep → pair → indep)\n", " (1, 1, 1, 1, 1) 0.0624 ████ (indep → indep → indep → indep → indep)\n", " (1, 3, 1) 0.0624 ████ (indep → 3-way → indep)\n", " (1, 1, 1, 2) 0.0624 ████ (indep → indep → indep → pair)\n", " (1, 4) 0.0624 ████ (indep → 4-way)\n", " (5,) 0.0624 ████ (5-way)\n", " (3, 1, 1) 0.0623 ████ (3-way → indep → indep)\n", " (3, 2) 0.0622 ████ (3-way → pair)\n", "\n", " Weight spread: min=0.0622 max=0.0627 std=0.000149 range=0.000586\n", " Uniform would be: 0.0625 = 0.0625\n", "\n", "=================================================================\n", "CONSENSUS GEOMETRY\n", "=================================================================\n", " cos(output, bert ): 0.8420\n", " cos(output, modern ): 0.8025\n", " cos(output, roberta ): 0.8192\n", " cos(output, albert ): 0.8026\n", " cos(output, distil ): 0.8376\n", " cos(output, centroid): 0.9088\n", "\n", " CV output: 0.0810\n", " CV bert : 0.3649\n", " CV modern : 0.3245\n", " CV roberta : 0.3559\n", " CV albert : 0.3863\n", " CV distil : 0.4015\n", "\n", " Equidistance check (should be ~equal):\n", " Range: 0.039510\n", " Std: 0.016736\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n", "\n", "#################################################################\n", "# SEED 42 COMPLETE\n", "#################################################################\n", "\n", "\n", " Seed: 43\n", "\n", " Loading cached embeddings...\n", " bert: torch.Size([20000, 768])\n", " modern: torch.Size([20000, 768])\n", " roberta: torch.Size([20000, 768])\n", " albert: torch.Size([20000, 768])\n", " distil: torch.Size([20000, 768])\n", "\n", "=================================================================\n", "PROCRUSTES ALIGNMENT (all → bert space)\n", "=================================================================\n", " bert : cos 1.0000 → 1.0000 (reference)\n", " modern : cos -0.0025 → 0.4849\n", " roberta : cos -0.0037 → 0.5138\n", " albert : cos -0.0004 → 0.4888\n", " distil : cos 0.8567 → 0.6557\n", "\n", " Pairwise cosines (post-alignment):\n", " bert ↔ modern : 0.8357\n", " bert ↔ roberta : 0.8685\n", " bert ↔ albert : 0.8413\n", " bert ↔ distil : 0.9314\n", " modern ↔ roberta : 0.8040\n", " modern ↔ albert : 0.7777\n", " modern ↔ distil : 0.8224\n", " roberta ↔ albert : 0.8039\n", " roberta ↔ distil : 0.8528\n", " albert ↔ distil : 0.8269\n", "\n", " Train: 18000, Val: 2000\n", "\n", "=================================================================\n", "MODEL: FiveAnchorModel + CompConv5d\n", "=================================================================\n", " Parameters: 16,910,352\n", " Conv5d paths: 16 (2^4 = 16)\n", "\n", "=================================================================\n", "TRAINING (20 epochs)\n", " 5 InfoNCE + 5 SVD + 1 CV = 11 losses\n", "=================================================================\n", " E 1: 4s loss=3.4673 acc=0.929/0.916/0.922/0.920/0.925 val=1.000/0.998/1.000/0.997/1.000 cv=0.120 top: (1, 2, 2)=0.0628 (4, 1)=0.0627 (1, 1, 1, 1, 1)=0.0627\n", " E 2: 4s loss=0.5333 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.116 top: (1, 2, 2)=0.0628 (4, 1)=0.0627 (1, 1, 1, 1, 1)=0.0627\n", " E 3: 4s loss=0.4559 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.114 top: (1, 2, 2)=0.0628 (4, 1)=0.0627 (1, 1, 1, 1, 1)=0.0627\n", " E 4: 4s loss=0.4218 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.106 top: (1, 2, 2)=0.0628 (4, 1)=0.0627 (1, 1, 1, 1, 1)=0.0627\n", " E 5: 4s loss=0.4062 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.100 top: (1, 2, 2)=0.0628 (4, 1)=0.0627 (1, 2, 1, 1)=0.0627\n", " E 6: 4s loss=0.3937 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.100 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0627\n", " E 7: 4s loss=0.3849 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.103 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0627\n", " E 8: 4s loss=0.3791 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.099 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0627\n", " E 9: 4s loss=0.3744 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.098 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0627\n", " E10: 4s loss=0.3697 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.091 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0627\n", " E11: 4s loss=0.3647 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.085 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0627\n", " E12: 4s loss=0.3607 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.091 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0627\n", " E13: 4s loss=0.3578 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.074 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", " E14: 4s loss=0.3572 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.080 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", " E15: 4s loss=0.3550 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.083 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", " E16: 4s loss=0.3544 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", " E17: 4s loss=0.3513 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.077 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", " E18: 4s loss=0.3507 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", " E19: 4s loss=0.3496 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", " E20: 4s loss=0.3505 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (4, 1)=0.0627 (1, 2, 2)=0.0627 (1, 2, 1, 1)=0.0626\n", "\n", "=================================================================\n", "FINAL PATH WEIGHT ANALYSIS (16 paths)\n", "=================================================================\n", " (4, 1) 0.0627 █████ (4-way → indep)\n", " (1, 2, 2) 0.0627 █████ (indep → pair → pair)\n", " (1, 2, 1, 1) 0.0626 █████ (indep → pair → indep → indep)\n", " (2, 3) 0.0626 █████ (pair → 3-way)\n", " (1, 4) 0.0626 █████ (indep → 4-way)\n", " (2, 1, 2) 0.0626 █████ (pair → indep → pair)\n", " (1, 1, 1, 1, 1) 0.0626 █████ (indep → indep → indep → indep → indep)\n", " (2, 2, 1) 0.0626 █████ (pair → pair → indep)\n", " (3, 1, 1) 0.0625 █████ (3-way → indep → indep)\n", " (3, 2) 0.0625 █████ (3-way → pair)\n", " (2, 1, 1, 1) 0.0625 ████ (pair → indep → indep → indep)\n", " (1, 1, 2, 1) 0.0624 ████ (indep → indep → pair → indep)\n", " (1, 3, 1) 0.0623 ████ (indep → 3-way → indep)\n", " (1, 1, 3) 0.0623 ████ (indep → indep → 3-way)\n", " (5,) 0.0623 ████ (5-way)\n", " (1, 1, 1, 2) 0.0623 ████ (indep → indep → indep → pair)\n", "\n", " Weight spread: min=0.0623 max=0.0627 std=0.000151 range=0.000448\n", " Uniform would be: 0.0625 = 0.0625\n", "\n", "=================================================================\n", "CONSENSUS GEOMETRY\n", "=================================================================\n", " cos(output, bert ): 0.8421\n", " cos(output, modern ): 0.8024\n", " cos(output, roberta ): 0.8192\n", " cos(output, albert ): 0.8027\n", " cos(output, distil ): 0.8377\n", " cos(output, centroid): 0.9089\n", "\n", " CV output: 0.0834\n", " CV bert : 0.3983\n", " CV modern : 0.3244\n", " CV roberta : 0.3226\n", " CV albert : 0.4035\n", " CV distil : 0.4327\n", "\n", " Equidistance check (should be ~equal):\n", " Range: 0.039687\n", " Std: 0.016773\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n", "\n", "#################################################################\n", "# SEED 43 COMPLETE\n", "#################################################################\n", "\n", "\n", " Seed: 44\n", "\n", " Loading cached embeddings...\n", " bert: torch.Size([20000, 768])\n", " modern: torch.Size([20000, 768])\n", " roberta: torch.Size([20000, 768])\n", " albert: torch.Size([20000, 768])\n", " distil: torch.Size([20000, 768])\n", "\n", "=================================================================\n", "PROCRUSTES ALIGNMENT (all → bert space)\n", "=================================================================\n", " bert : cos 1.0000 → 1.0000 (reference)\n", " modern : cos -0.0025 → 0.4849\n", " roberta : cos -0.0037 → 0.5138\n", " albert : cos -0.0004 → 0.4888\n", " distil : cos 0.8567 → 0.6557\n", "\n", " Pairwise cosines (post-alignment):\n", " bert ↔ modern : 0.8357\n", " bert ↔ roberta : 0.8685\n", " bert ↔ albert : 0.8413\n", " bert ↔ distil : 0.9314\n", " modern ↔ roberta : 0.8040\n", " modern ↔ albert : 0.7777\n", " modern ↔ distil : 0.8224\n", " roberta ↔ albert : 0.8039\n", " roberta ↔ distil : 0.8528\n", " albert ↔ distil : 0.8269\n", "\n", " Train: 18000, Val: 2000\n", "\n", "=================================================================\n", "MODEL: FiveAnchorModel + CompConv5d\n", "=================================================================\n", " Parameters: 16,910,352\n", " Conv5d paths: 16 (2^4 = 16)\n", "\n", "=================================================================\n", "TRAINING (20 epochs)\n", " 5 InfoNCE + 5 SVD + 1 CV = 11 losses\n", "=================================================================\n", " E 1: 4s loss=3.5689 acc=0.922/0.908/0.916/0.913/0.920 val=1.000/0.998/1.000/0.998/1.000 cv=0.121 top: (2, 1, 1, 1)=0.0627 (1, 1, 2, 1)=0.0627 (4, 1)=0.0627\n", " E 2: 4s loss=0.5315 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.109 top: (2, 1, 1, 1)=0.0628 (1, 1, 2, 1)=0.0627 (4, 1)=0.0627\n", " E 3: 4s loss=0.4524 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.103 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (1, 1, 2, 1)=0.0627\n", " E 4: 4s loss=0.4229 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.100 top: (2, 1, 1, 1)=0.0629 (4, 1)=0.0627 (1, 1, 2, 1)=0.0627\n", " E 5: 4s loss=0.4060 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.093 top: (2, 1, 1, 1)=0.0629 (4, 1)=0.0627 (1, 1, 2, 1)=0.0627\n", " E 6: 4s loss=0.3944 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.093 top: (2, 1, 1, 1)=0.0629 (4, 1)=0.0627 (1, 1, 2, 1)=0.0627\n", " E 7: 4s loss=0.3835 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.090 top: (2, 1, 1, 1)=0.0629 (4, 1)=0.0627 (2, 1, 2)=0.0627\n", " E 8: 4s loss=0.3782 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.086 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 1, 2)=0.0627\n", " E 9: 4s loss=0.3759 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.091 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 1, 2)=0.0626\n", " E10: 4s loss=0.3679 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.099 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E11: 4s loss=0.3638 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.091 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E12: 4s loss=0.3616 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.091 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E13: 4s loss=0.3597 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.080 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E14: 4s loss=0.3586 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.078 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E15: 4s loss=0.3550 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.088 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E16: 4s loss=0.3537 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.086 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E17: 4s loss=0.3530 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.081 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0627\n", " E18: 4s loss=0.3518 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.087 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E19: 4s loss=0.3499 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.089 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", " E20: 4s loss=0.3488 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.089 top: (2, 1, 1, 1)=0.0628 (4, 1)=0.0627 (2, 3)=0.0626\n", "\n", "=================================================================\n", "FINAL PATH WEIGHT ANALYSIS (16 paths)\n", "=================================================================\n", " (2, 1, 1, 1) 0.0628 █████ (pair → indep → indep → indep)\n", " (4, 1) 0.0627 █████ (4-way → indep)\n", " (2, 3) 0.0626 █████ (pair → 3-way)\n", " (1, 1, 3) 0.0626 █████ (indep → indep → 3-way)\n", " (2, 1, 2) 0.0626 █████ (pair → indep → pair)\n", " (1, 1, 2, 1) 0.0626 █████ (indep → indep → pair → indep)\n", " (2, 2, 1) 0.0625 █████ (pair → pair → indep)\n", " (1, 4) 0.0625 █████ (indep → 4-way)\n", " (1, 2, 1, 1) 0.0625 ████ (indep → pair → indep → indep)\n", " (1, 3, 1) 0.0625 ████ (indep → 3-way → indep)\n", " (1, 1, 1, 1, 1) 0.0624 ████ (indep → indep → indep → indep → indep)\n", " (1, 1, 1, 2) 0.0624 ████ (indep → indep → indep → pair)\n", " (3, 1, 1) 0.0624 ████ (3-way → indep → indep)\n", " (5,) 0.0623 ████ (5-way)\n", " (1, 2, 2) 0.0623 ████ (indep → pair → pair)\n", " (3, 2) 0.0623 ████ (3-way → pair)\n", "\n", " Weight spread: min=0.0623 max=0.0628 std=0.000133 range=0.000484\n", " Uniform would be: 0.0625 = 0.0625\n", "\n", "=================================================================\n", "CONSENSUS GEOMETRY\n", "=================================================================\n", " cos(output, bert ): 0.8422\n", " cos(output, modern ): 0.8026\n", " cos(output, roberta ): 0.8192\n", " cos(output, albert ): 0.8029\n", " cos(output, distil ): 0.8377\n", " cos(output, centroid): 0.9089\n", "\n", " CV output: 0.0913\n", " CV bert : 0.4204\n", " CV modern : 0.3488\n", " CV roberta : 0.3288\n", " CV albert : 0.3748\n", " CV distil : 0.3881\n", "\n", " Equidistance check (should be ~equal):\n", " Range: 0.039557\n", " Std: 0.016713\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n", "\n", "#################################################################\n", "# SEED 44 COMPLETE\n", "#################################################################\n", "\n", "\n", " Seed: 12341\n", "\n", " Loading cached embeddings...\n", " bert: torch.Size([20000, 768])\n", " modern: torch.Size([20000, 768])\n", " roberta: torch.Size([20000, 768])\n", " albert: torch.Size([20000, 768])\n", " distil: torch.Size([20000, 768])\n", "\n", "=================================================================\n", "PROCRUSTES ALIGNMENT (all → bert space)\n", "=================================================================\n", " bert : cos 1.0000 → 1.0000 (reference)\n", " modern : cos -0.0025 → 0.4849\n", " roberta : cos -0.0037 → 0.5138\n", " albert : cos -0.0004 → 0.4888\n", " distil : cos 0.8567 → 0.6557\n", "\n", " Pairwise cosines (post-alignment):\n", " bert ↔ modern : 0.8357\n", " bert ↔ roberta : 0.8685\n", " bert ↔ albert : 0.8413\n", " bert ↔ distil : 0.9314\n", " modern ↔ roberta : 0.8040\n", " modern ↔ albert : 0.7777\n", " modern ↔ distil : 0.8224\n", " roberta ↔ albert : 0.8039\n", " roberta ↔ distil : 0.8528\n", " albert ↔ distil : 0.8269\n", "\n", " Train: 18000, Val: 2000\n", "\n", "=================================================================\n", "MODEL: FiveAnchorModel + CompConv5d\n", "=================================================================\n", " Parameters: 16,910,352\n", " Conv5d paths: 16 (2^4 = 16)\n", "\n", "=================================================================\n", "TRAINING (20 epochs)\n", " 5 InfoNCE + 5 SVD + 1 CV = 11 losses\n", "=================================================================\n", " E 1: 4s loss=3.6163 acc=0.924/0.908/0.915/0.914/0.920 val=1.000/0.999/1.000/1.000/1.000 cv=0.121 top: (2, 1, 1, 1)=0.0628 (2, 2, 1)=0.0627 (2, 3)=0.0627\n", " E 2: 4s loss=0.5378 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.114 top: (2, 1, 1, 1)=0.0628 (2, 2, 1)=0.0628 (2, 3)=0.0627\n", " E 3: 4s loss=0.4537 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.103 top: (2, 1, 1, 1)=0.0629 (2, 2, 1)=0.0628 (2, 3)=0.0628\n", " E 4: 4s loss=0.4206 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.106 top: (2, 1, 1, 1)=0.0628 (2, 3)=0.0628 (2, 2, 1)=0.0628\n", " E 5: 4s loss=0.4046 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.086 top: (2, 1, 1, 1)=0.0628 (2, 3)=0.0628 (2, 2, 1)=0.0628\n", " E 6: 4s loss=0.3930 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.100 top: (2, 3)=0.0628 (2, 1, 1, 1)=0.0628 (2, 2, 1)=0.0628\n", " E 7: 4s loss=0.3839 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.092 top: (2, 3)=0.0628 (2, 1, 1, 1)=0.0628 (2, 2, 1)=0.0628\n", " E 8: 4s loss=0.3787 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.092 top: (2, 3)=0.0628 (2, 1, 1, 1)=0.0628 (2, 2, 1)=0.0627\n", " E 9: 4s loss=0.3735 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.080 top: (2, 3)=0.0628 (2, 1, 1, 1)=0.0627 (2, 2, 1)=0.0627\n", " E10: 4s loss=0.3689 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.083 top: (2, 3)=0.0628 (2, 1, 1, 1)=0.0627 (2, 2, 1)=0.0627\n", " E11: 4s loss=0.3650 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.088 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (2, 1, 1, 1)=0.0627\n", " E12: 4s loss=0.3623 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.080 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (2, 1, 1, 1)=0.0627\n", " E13: 4s loss=0.3591 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (2, 1, 1, 1)=0.0627\n", " E14: 4s loss=0.3565 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.091 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (2, 1, 1, 1)=0.0627\n", " E15: 4s loss=0.3558 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (2, 1, 1, 1)=0.0626\n", " E16: 4s loss=0.3537 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.081 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (2, 1, 1, 1)=0.0626\n", " E17: 4s loss=0.3532 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (4, 1)=0.0626\n", " E18: 4s loss=0.3509 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (4, 1)=0.0626\n", " E19: 4s loss=0.3502 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.071 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (4, 1)=0.0626\n", " E20: 4s loss=0.3505 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.089 top: (2, 3)=0.0628 (2, 2, 1)=0.0627 (4, 1)=0.0626\n", "\n", "=================================================================\n", "FINAL PATH WEIGHT ANALYSIS (16 paths)\n", "=================================================================\n", " (2, 3) 0.0628 █████ (pair → 3-way)\n", " (2, 2, 1) 0.0627 █████ (pair → pair → indep)\n", " (4, 1) 0.0626 █████ (4-way → indep)\n", " (2, 1, 1, 1) 0.0626 █████ (pair → indep → indep → indep)\n", " (1, 1, 2, 1) 0.0626 █████ (indep → indep → pair → indep)\n", " (1, 4) 0.0625 █████ (indep → 4-way)\n", " (1, 2, 2) 0.0625 █████ (indep → pair → pair)\n", " (2, 1, 2) 0.0625 █████ (pair → indep → pair)\n", " (3, 1, 1) 0.0625 ████ (3-way → indep → indep)\n", " (1, 1, 3) 0.0625 ████ (indep → indep → 3-way)\n", " (1, 3, 1) 0.0624 ████ (indep → 3-way → indep)\n", " (1, 2, 1, 1) 0.0624 ████ (indep → pair → indep → indep)\n", " (5,) 0.0624 ████ (5-way)\n", " (1, 1, 1, 2) 0.0623 ████ (indep → indep → indep → pair)\n", " (3, 2) 0.0623 ████ (3-way → pair)\n", " (1, 1, 1, 1, 1) 0.0623 ████ (indep → indep → indep → indep → indep)\n", "\n", " Weight spread: min=0.0623 max=0.0628 std=0.000140 range=0.000531\n", " Uniform would be: 0.0625 = 0.0625\n", "\n", "=================================================================\n", "CONSENSUS GEOMETRY\n", "=================================================================\n", " cos(output, bert ): 0.8419\n", " cos(output, modern ): 0.8026\n", " cos(output, roberta ): 0.8192\n", " cos(output, albert ): 0.8028\n", " cos(output, distil ): 0.8377\n", " cos(output, centroid): 0.9088\n", "\n", " CV output: 0.0791\n", " CV bert : 0.3944\n", " CV modern : 0.3594\n", " CV roberta : 0.3726\n", " CV albert : 0.3543\n", " CV distil : 0.4075\n", "\n", " Equidistance check (should be ~equal):\n", " Range: 0.039320\n", " Std: 0.016674\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n", "\n", "#################################################################\n", "# SEED 12341 COMPLETE\n", "#################################################################\n", "\n", "\n", " Seed: 12323\n", "\n", " Loading cached embeddings...\n", " bert: torch.Size([20000, 768])\n", " modern: torch.Size([20000, 768])\n", " roberta: torch.Size([20000, 768])\n", " albert: torch.Size([20000, 768])\n", " distil: torch.Size([20000, 768])\n", "\n", "=================================================================\n", "PROCRUSTES ALIGNMENT (all → bert space)\n", "=================================================================\n", " bert : cos 1.0000 → 1.0000 (reference)\n", " modern : cos -0.0025 → 0.4849\n", " roberta : cos -0.0037 → 0.5138\n", " albert : cos -0.0004 → 0.4888\n", " distil : cos 0.8567 → 0.6557\n", "\n", " Pairwise cosines (post-alignment):\n", " bert ↔ modern : 0.8357\n", " bert ↔ roberta : 0.8685\n", " bert ↔ albert : 0.8413\n", " bert ↔ distil : 0.9314\n", " modern ↔ roberta : 0.8040\n", " modern ↔ albert : 0.7777\n", " modern ↔ distil : 0.8224\n", " roberta ↔ albert : 0.8039\n", " roberta ↔ distil : 0.8528\n", " albert ↔ distil : 0.8269\n", "\n", " Train: 18000, Val: 2000\n", "\n", "=================================================================\n", "MODEL: FiveAnchorModel + CompConv5d\n", "=================================================================\n", " Parameters: 16,910,352\n", " Conv5d paths: 16 (2^4 = 16)\n", "\n", "=================================================================\n", "TRAINING (20 epochs)\n", " 5 InfoNCE + 5 SVD + 1 CV = 11 losses\n", "=================================================================\n", " E 1: 4s loss=3.6015 acc=0.924/0.908/0.916/0.913/0.919 val=1.000/0.998/1.000/0.999/1.000 cv=0.130 top: (1, 1, 1, 2)=0.0627 (3, 1, 1)=0.0627 (4, 1)=0.0627\n", " E 2: 4s loss=0.5371 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/0.999/1.000/0.999/1.000 cv=0.105 top: (1, 1, 1, 2)=0.0628 (3, 1, 1)=0.0628 (4, 1)=0.0627\n", " E 3: 4s loss=0.4539 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.104 top: (1, 1, 1, 2)=0.0628 (3, 1, 1)=0.0628 (4, 1)=0.0627\n", " E 4: 4s loss=0.4210 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.093 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0628 (4, 1)=0.0627\n", " E 5: 4s loss=0.4032 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.104 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0628 (4, 1)=0.0627\n", " E 6: 4s loss=0.3938 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.091 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0628 (4, 1)=0.0627\n", " E 7: 4s loss=0.3846 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.094 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0628 (4, 1)=0.0627\n", " E 8: 4s loss=0.3765 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.094 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0628 (4, 1)=0.0627\n", " E 9: 4s loss=0.3710 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.093 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0628 (4, 1)=0.0627\n", " E10: 4s loss=0.3683 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.096 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0627 (4, 1)=0.0627\n", " E11: 4s loss=0.3655 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.085 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0627 (4, 1)=0.0627\n", " E12: 4s loss=0.3611 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.095 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0627 (4, 1)=0.0627\n", " E13: 4s loss=0.3595 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.090 top: (3, 1, 1)=0.0629 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", " E14: 4s loss=0.3571 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", " E15: 4s loss=0.3549 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", " E16: 4s loss=0.3540 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.087 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", " E17: 4s loss=0.3527 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", " E18: 4s loss=0.3514 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.083 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", " E19: 4s loss=0.3508 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.078 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", " E20: 4s loss=0.3508 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (3, 1, 1)=0.0628 (1, 1, 1, 2)=0.0627 (4, 1)=0.0626\n", "\n", "=================================================================\n", "FINAL PATH WEIGHT ANALYSIS (16 paths)\n", "=================================================================\n", " (3, 1, 1) 0.0628 █████ (3-way → indep → indep)\n", " (1, 1, 1, 2) 0.0627 █████ (indep → indep → indep → pair)\n", " (4, 1) 0.0626 █████ (4-way → indep)\n", " (1, 4) 0.0626 █████ (indep → 4-way)\n", " (2, 3) 0.0626 █████ (pair → 3-way)\n", " (2, 2, 1) 0.0626 █████ (pair → pair → indep)\n", " (2, 1, 1, 1) 0.0625 █████ (pair → indep → indep → indep)\n", " (1, 1, 3) 0.0625 ████ (indep → indep → 3-way)\n", " (1, 2, 1, 1) 0.0625 ████ (indep → pair → indep → indep)\n", " (1, 1, 1, 1, 1) 0.0625 ████ (indep → indep → indep → indep → indep)\n", " (2, 1, 2) 0.0624 ████ (pair → indep → pair)\n", " (1, 3, 1) 0.0624 ████ (indep → 3-way → indep)\n", " (3, 2) 0.0624 ████ (3-way → pair)\n", " (1, 1, 2, 1) 0.0623 ████ (indep → indep → pair → indep)\n", " (1, 2, 2) 0.0623 ████ (indep → pair → pair)\n", " (5,) 0.0622 ████ (5-way)\n", "\n", " Weight spread: min=0.0622 max=0.0628 std=0.000154 range=0.000621\n", " Uniform would be: 0.0625 = 0.0625\n", "\n", "=================================================================\n", "CONSENSUS GEOMETRY\n", "=================================================================\n", " cos(output, bert ): 0.8415\n", " cos(output, modern ): 0.8021\n", " cos(output, roberta ): 0.8190\n", " cos(output, albert ): 0.8024\n", " cos(output, distil ): 0.8374\n", " cos(output, centroid): 0.9085\n", "\n", " CV output: 0.0834\n", " CV bert : 0.3988\n", " CV modern : 0.3166\n", " CV roberta : 0.3643\n", " CV albert : 0.3431\n", " CV distil : 0.3915\n", "\n", " Equidistance check (should be ~equal):\n", " Range: 0.039444\n", " Std: 0.016707\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n", "\n", "#################################################################\n", "# SEED 12323 COMPLETE\n", "#################################################################\n", "\n", "\n", " Seed: 8675309\n", "\n", " Loading cached embeddings...\n", " bert: torch.Size([20000, 768])\n", " modern: torch.Size([20000, 768])\n", " roberta: torch.Size([20000, 768])\n", " albert: torch.Size([20000, 768])\n", " distil: torch.Size([20000, 768])\n", "\n", "=================================================================\n", "PROCRUSTES ALIGNMENT (all → bert space)\n", "=================================================================\n", " bert : cos 1.0000 → 1.0000 (reference)\n", " modern : cos -0.0025 → 0.4849\n", " roberta : cos -0.0037 → 0.5138\n", " albert : cos -0.0004 → 0.4888\n", " distil : cos 0.8567 → 0.6557\n", "\n", " Pairwise cosines (post-alignment):\n", " bert ↔ modern : 0.8357\n", " bert ↔ roberta : 0.8685\n", " bert ↔ albert : 0.8413\n", " bert ↔ distil : 0.9314\n", " modern ↔ roberta : 0.8040\n", " modern ↔ albert : 0.7777\n", " modern ↔ distil : 0.8224\n", " roberta ↔ albert : 0.8039\n", " roberta ↔ distil : 0.8528\n", " albert ↔ distil : 0.8269\n", "\n", " Train: 18000, Val: 2000\n", "\n", "=================================================================\n", "MODEL: FiveAnchorModel + CompConv5d\n", "=================================================================\n", " Parameters: 16,910,352\n", " Conv5d paths: 16 (2^4 = 16)\n", "\n", "=================================================================\n", "TRAINING (20 epochs)\n", " 5 InfoNCE + 5 SVD + 1 CV = 11 losses\n", "=================================================================\n", " E 1: 4s loss=3.5333 acc=0.926/0.910/0.918/0.916/0.923 val=1.000/1.000/1.000/0.998/1.000 cv=0.125 top: (4, 1)=0.0629 (1, 1, 2, 1)=0.0628 (2, 1, 2)=0.0627\n", " E 2: 4s loss=0.5396 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.116 top: (4, 1)=0.0629 (1, 1, 2, 1)=0.0629 (2, 1, 2)=0.0627\n", " E 3: 4s loss=0.4549 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.109 top: (4, 1)=0.0629 (1, 1, 2, 1)=0.0629 (2, 1, 2)=0.0627\n", " E 4: 4s loss=0.4246 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.098 top: (1, 1, 2, 1)=0.0629 (4, 1)=0.0629 (2, 1, 2)=0.0627\n", " E 5: 4s loss=0.4077 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.100 top: (1, 1, 2, 1)=0.0629 (4, 1)=0.0629 (2, 1, 2)=0.0627\n", " E 6: 4s loss=0.3953 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.095 top: (1, 1, 2, 1)=0.0629 (4, 1)=0.0629 (2, 1, 2)=0.0627\n", " E 7: 4s loss=0.3850 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.095 top: (1, 1, 2, 1)=0.0629 (4, 1)=0.0629 (2, 1, 2)=0.0627\n", " E 8: 4s loss=0.3780 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.098 top: (1, 1, 2, 1)=0.0629 (4, 1)=0.0629 (2, 1, 2)=0.0627\n", " E 9: 4s loss=0.3729 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.101 top: (4, 1)=0.0629 (1, 1, 2, 1)=0.0629 (2, 1, 2)=0.0626\n", " E10: 4s loss=0.3697 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.090 top: (4, 1)=0.0629 (1, 1, 2, 1)=0.0628 (2, 3)=0.0626\n", " E11: 4s loss=0.3650 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.082 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0626\n", " E12: 4s loss=0.3632 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0626\n", " E13: 4s loss=0.3610 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0626\n", " E14: 4s loss=0.3587 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.073 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0627\n", " E15: 4s loss=0.3542 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.097 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0627\n", " E16: 4s loss=0.3537 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.086 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0627\n", " E17: 4s loss=0.3538 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.083 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0627\n", " E18: 4s loss=0.3514 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.084 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0627\n", " E19: 4s loss=0.3504 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.085 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0627\n", " E20: 4s loss=0.3506 acc=1.000/1.000/1.000/1.000/1.000 val=1.000/1.000/1.000/1.000/1.000 cv=0.077 top: (4, 1)=0.0628 (1, 1, 2, 1)=0.0628 (2, 3)=0.0627\n", "\n", "=================================================================\n", "FINAL PATH WEIGHT ANALYSIS (16 paths)\n", "=================================================================\n", " (4, 1) 0.0628 █████ (4-way → indep)\n", " (1, 1, 2, 1) 0.0628 █████ (indep → indep → pair → indep)\n", " (2, 3) 0.0627 █████ (pair → 3-way)\n", " (2, 1, 2) 0.0626 █████ (pair → indep → pair)\n", " (3, 2) 0.0626 █████ (3-way → pair)\n", " (1, 2, 1, 1) 0.0625 █████ (indep → pair → indep → indep)\n", " (1, 3, 1) 0.0625 █████ (indep → 3-way → indep)\n", " (1, 1, 1, 2) 0.0625 █████ (indep → indep → indep → pair)\n", " (1, 4) 0.0625 ████ (indep → 4-way)\n", " (1, 1, 3) 0.0625 ████ (indep → indep → 3-way)\n", " (2, 2, 1) 0.0624 ████ (pair → pair → indep)\n", " (2, 1, 1, 1) 0.0623 ████ (pair → indep → indep → indep)\n", " (1, 1, 1, 1, 1) 0.0623 ████ (indep → indep → indep → indep → indep)\n", " (3, 1, 1) 0.0623 ████ (3-way → indep → indep)\n", " (5,) 0.0623 ████ (5-way)\n", " (1, 2, 2) 0.0623 ████ (indep → pair → pair)\n", "\n", " Weight spread: min=0.0623 max=0.0628 std=0.000159 range=0.000530\n", " Uniform would be: 0.0625 = 0.0625\n", "\n", "=================================================================\n", "CONSENSUS GEOMETRY\n", "=================================================================\n", " cos(output, bert ): 0.8418\n", " cos(output, modern ): 0.8024\n", " cos(output, roberta ): 0.8190\n", " cos(output, albert ): 0.8027\n", " cos(output, distil ): 0.8374\n", " cos(output, centroid): 0.9087\n", "\n", " CV output: 0.0835\n", " CV bert : 0.4045\n", " CV modern : 0.3322\n", " CV roberta : 0.4071\n", " CV albert : 0.3628\n", " CV distil : 0.4071\n", "\n", " Equidistance check (should be ~equal):\n", " Range: 0.039413\n", " Std: 0.016641\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n", "\n", "#################################################################\n", "# SEED 8675309 COMPLETE\n", "#################################################################\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# five bert distillation paradigm" ], "metadata": { "id": "jnhe7llZaaGc" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# DISTILLED CONSENSUS BERT: Standalone Caption Encoder\n", "#\n", "# Distills the five-BERT pentachoron consensus into a small standalone\n", "# transformer that requires NO expert models at inference.\n", "#\n", "# Pipeline:\n", "# 1. Generate consensus embeddings from cached five-BERT data\n", "# 2. Train a small transformer to reproduce them from raw text\n", "# 3. At inference: just the small model + tokenizer\n", "#\n", "# Target domain: image captions (CC12M), optimized for caption similarity\n", "# ============================================================================\n", "\n", "import math\n", "import os\n", "import time\n", "import json\n", "from dataclasses import dataclass\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"DISTILLED CONSENSUS BERT\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STEP 1: GENERATE CONSENSUS TARGETS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def load_consensus_targets():\n", " \"\"\"\n", " Load five-BERT embeddings, Procrustes align, run consensus model,\n", " produce target embeddings for all captions.\n", " \"\"\"\n", " cache_dir = \"/home/claude/five_berts_cache\"\n", " targets_path = os.path.join(cache_dir, \"consensus_targets.pt\")\n", " captions_path = os.path.join(cache_dir, \"captions.json\")\n", "\n", " # Check if targets already generated\n", " if os.path.exists(targets_path):\n", " print(\"\\n Loading cached consensus targets...\")\n", " data = torch.load(targets_path, weights_only=True)\n", " with open(captions_path) as f:\n", " captions = json.load(f)\n", " print(f\" Targets: {data.shape}, Captions: {len(captions)}\")\n", " return data, captions\n", "\n", " print(\"\\n Generating consensus targets from five-BERT cache...\")\n", "\n", " # Load raw embeddings\n", " model_names = [\"bert\", \"modern\", \"roberta\", \"albert\", \"distil\"]\n", " raw = {}\n", " for name in model_names:\n", " raw[name] = torch.load(os.path.join(cache_dir, f\"{name}.pt\"), weights_only=True)\n", " print(f\" {name}: {raw[name].shape}\")\n", "\n", " with open(captions_path) as f:\n", " captions = json.load(f)\n", "\n", " # Whitened Procrustes alignment (same as five_berts.py)\n", " def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", " def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float()\n", " T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True)\n", " t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean\n", " Tc = T - t_mean\n", " N_s, D = Sc.shape\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " t_unwhiten = torch.linalg.pinv(t_whiten)\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten, \"target_unwhitener\": t_unwhiten,\n", " }\n", "\n", " def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]\n", " x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]\n", " return x\n", "\n", " # Align all to bert space\n", " ref = raw[\"bert\"]\n", " aligned = {}\n", " for name in model_names:\n", " info = procrustes_align(raw[name], ref)\n", " aligned[name] = apply_align(raw[name], info)\n", " print(f\" Aligned {name}\")\n", "\n", " # Load consensus model\n", " #from five_berts import FiveAnchorModel, CompConv5d, ConvPath, integer_compositions\n", " model = FiveAnchorModel(768, 256, 5).to(DEVICE)\n", " # Load best seed\n", " for seed in [42, 43, 44]:\n", " p = os.path.join(cache_dir, f\"five_model_seed{seed}.pt\")\n", " if os.path.exists(p):\n", " model.load_state_dict(torch.load(p, weights_only=True, map_location=DEVICE))\n", " print(f\" Loaded consensus model (seed {seed})\")\n", " break\n", "\n", " # Generate targets\n", " model.eval()\n", " all_targets = []\n", " batch_size = 512\n", " N = aligned[\"bert\"].shape[0]\n", "\n", " with torch.no_grad():\n", " for i in tqdm(range(0, N, batch_size), desc=\" Generating targets\"):\n", " j = min(i + batch_size, N)\n", " anchors = [aligned[name][i:j].to(DEVICE) for name in model_names]\n", " out = model(anchors)\n", " all_targets.append(out.cpu())\n", "\n", " targets = torch.cat(all_targets)\n", " print(f\" Consensus targets: {targets.shape}\")\n", "\n", " # Cache\n", " torch.save(targets, targets_path)\n", " print(f\" Saved to {targets_path}\")\n", "\n", " return targets, captions\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STEP 2: STUDENT MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " \"\"\"\n", " Small standalone transformer for caption encoding.\n", " No pretrained weights — trained from scratch on consensus targets.\n", "\n", " Architecture:\n", " - Learned token embeddings (WordPiece vocab)\n", " - Learned position embeddings (512 max)\n", " - N transformer encoder layers\n", " - Mean pooling → projection → L2-normalized output\n", " \"\"\"\n", " def __init__(self, vocab_size=30522, max_len=128, d_model=384,\n", " n_heads=6, n_layers=4, d_ff=1536, output_dim=768,\n", " dropout=0.1, pad_token_id=0):\n", " super().__init__()\n", " self.pad_token_id = pad_token_id\n", " self.d_model = d_model\n", "\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff,\n", " dropout=dropout, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n", "\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model),\n", " nn.GELU(),\n", " nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim),\n", " )\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " B, L = input_ids.shape\n", " positions = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", "\n", " x = self.token_emb(input_ids) + self.pos_emb(positions)\n", " x = self.emb_drop(self.emb_norm(x))\n", "\n", " # Transformer mask\n", " if attention_mask is not None:\n", " src_key_padding_mask = ~attention_mask.bool()\n", " else:\n", " src_key_padding_mask = (input_ids == self.pad_token_id)\n", "\n", " x = self.encoder(x, src_key_padding_mask=src_key_padding_mask)\n", "\n", " # Mean pool over non-padding tokens\n", " if attention_mask is not None:\n", " mask = attention_mask.unsqueeze(-1).float()\n", " else:\n", " mask = (~src_key_padding_mask).unsqueeze(-1).float()\n", " pooled = (x * mask).sum(1) / mask.sum(1).clamp(min=1)\n", "\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.084, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class TrainConfig:\n", " # Student architecture\n", " d_model: int = 384\n", " n_heads: int = 6\n", " n_layers: int = 4\n", " d_ff: int = 1536\n", " max_len: int = 128\n", " output_dim: int = 768\n", " dropout: float = 0.1\n", "\n", " # Training\n", " epochs: int = 20\n", " batch_size: int = 256\n", " lr: float = 3e-4\n", " weight_decay: float = 0.01\n", " warmup_steps: int = 200\n", " grad_clip: float = 1.0\n", " seed: int = 42\n", "\n", " # Loss\n", " nce_weight: float = 1.0\n", " mse_weight: float = 1.0\n", " cv_weight: float = 0.1\n", " cv_target: float = 0.084 # consensus CV\n", "\n", " # Data\n", " n_val: int = 2000\n", "\n", " # Paths\n", " save_dir: str = \"/home/claude/distilled_consensus\"\n", "\n", "TCFG = TrainConfig()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train():\n", " torch.manual_seed(TCFG.seed)\n", " torch.cuda.manual_seed_all(TCFG.seed)\n", " np.random.seed(TCFG.seed)\n", "\n", " # ── Targets ──\n", " targets, captions = load_consensus_targets()\n", "\n", " # ── Tokenizer (BERT WordPiece — same vocab the consensus was built on) ──\n", " from transformers import AutoTokenizer\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " print(f\"\\n Tokenizer: bert-base-uncased (vocab={tokenizer.vocab_size})\")\n", "\n", " # Pre-tokenize everything\n", " print(\" Pre-tokenizing...\")\n", " all_tokens = tokenizer(\n", " captions, max_length=TCFG.max_len, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = all_tokens[\"input_ids\"]\n", " attention_mask = all_tokens[\"attention_mask\"]\n", "\n", " # Token length stats\n", " real_lens = attention_mask.sum(1).float()\n", " print(f\" Token lengths: mean={real_lens.mean():.0f} \"\n", " f\"median={real_lens.median():.0f} max={real_lens.max():.0f} \"\n", " f\">{TCFG.max_len}: {(real_lens >= TCFG.max_len).float().mean():.1%}\")\n", "\n", " # Split\n", " n_train = len(captions) - TCFG.n_val\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = targets[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = targets[n_train:].to(DEVICE)\n", " print(f\" Train: {n_train}, Val: {TCFG.n_val}\")\n", "\n", " # ── Student model ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"STUDENT MODEL\")\n", " print(f\"{'='*65}\")\n", "\n", " student = CaptionEncoder(\n", " vocab_size=tokenizer.vocab_size,\n", " max_len=TCFG.max_len,\n", " d_model=TCFG.d_model,\n", " n_heads=TCFG.n_heads,\n", " n_layers=TCFG.n_layers,\n", " d_ff=TCFG.d_ff,\n", " output_dim=TCFG.output_dim,\n", " dropout=TCFG.dropout,\n", " pad_token_id=tokenizer.pad_token_id,\n", " ).to(DEVICE)\n", "\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Architecture: {TCFG.n_layers} layers, {TCFG.d_model}-dim, \"\n", " f\"{TCFG.n_heads} heads, {TCFG.d_ff} FFN\")\n", " print(f\" Output: {TCFG.output_dim}-dim (matches consensus)\")\n", " print(f\" Parameters: {n_params:,}\")\n", "\n", " # ── Optimizer ──\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=TCFG.lr,\n", " weight_decay=TCFG.weight_decay)\n", " n_batches = n_train // TCFG.batch_size\n", " total_steps = n_batches * TCFG.epochs\n", " scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=TCFG.warmup_steps),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - TCFG.warmup_steps, 1),\n", " eta_min=1e-6)],\n", " milestones=[TCFG.warmup_steps])\n", "\n", " os.makedirs(TCFG.save_dir, exist_ok=True)\n", "\n", " # ── Train ──\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({TCFG.epochs} epochs)\")\n", " print(f\" Losses: InfoNCE + MSE + pentachoron CV (target={TCFG.cv_target})\")\n", " print(f\"{'='*65}\")\n", "\n", " all_metrics = {\"config\": vars(TCFG), \"epochs\": []}\n", " best_val_cos = 0.0\n", "\n", " for epoch in range(TCFG.epochs):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " losses = {\"total\": 0, \"nce\": 0, \"mse\": 0, \"cv\": 0}\n", " metrics = {\"acc\": 0, \"cos\": 0}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, TCFG.batch_size):\n", " idx = perm[i:i+TCFG.batch_size]\n", " if len(idx) < 8: continue\n", "\n", " ids = train_ids[idx]\n", " mask = train_mask[idx]\n", " tgt = train_targets[idx]\n", "\n", " emb = student(ids, mask)\n", "\n", " # InfoNCE: student should retrieve correct consensus target\n", " l_nce, acc = infonce(emb, tgt)\n", "\n", " # MSE: direct regression on normalized embeddings\n", " l_mse = F.mse_loss(emb, tgt)\n", "\n", " # CV: student embedding space should match consensus geometry\n", " l_cv = cv_loss(emb, target=TCFG.cv_target)\n", "\n", " loss = (TCFG.nce_weight * l_nce +\n", " TCFG.mse_weight * l_mse +\n", " TCFG.cv_weight * l_cv)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), TCFG.grad_clip)\n", " optimizer.step()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", "\n", " losses[\"total\"] += loss.item()\n", " losses[\"nce\"] += l_nce.item()\n", " losses[\"mse\"] += l_mse.item()\n", " metrics[\"acc\"] += acc\n", " metrics[\"cos\"] += cos\n", " n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", "\n", " # Validation\n", " student.eval()\n", " with torch.no_grad():\n", " # Process val in chunks to avoid OOM\n", " val_embs = []\n", " for vi in range(0, TCFG.n_val, 512):\n", " vj = min(vi + 512, TCFG.n_val)\n", " ve = student(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(ve)\n", " val_emb = torch.cat(val_embs)\n", "\n", " _, val_acc = infonce(val_emb, val_targets)\n", " val_cos = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", " val_cv = cv_metric(val_emb)\n", "\n", " summary = {\n", " \"epoch\": epoch + 1, \"elapsed\": elapsed,\n", " \"loss\": losses[\"total\"] / d,\n", " \"train_acc\": metrics[\"acc\"] / d,\n", " \"train_cos\": metrics[\"cos\"] / d,\n", " \"val_acc\": val_acc,\n", " \"val_cos\": val_cos,\n", " \"val_cv\": val_cv,\n", " }\n", " all_metrics[\"epochs\"].append(summary)\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s \"\n", " f\"loss={summary['loss']:.4f} \"\n", " f\"t_acc={summary['train_acc']:.3f} t_cos={summary['train_cos']:.3f} \"\n", " f\"v_acc={summary['val_acc']:.3f} v_cos={summary['val_cos']:.3f} \"\n", " f\"v_cv={summary['val_cv']:.3f}\")\n", "\n", " # Save best\n", " if val_cos > best_val_cos:\n", " best_val_cos = val_cos\n", " torch.save(student.state_dict(),\n", " os.path.join(TCFG.save_dir, \"best_model.pt\"))\n", "\n", " # Save every 5 epochs\n", " if (epoch + 1) % 5 == 0:\n", " torch.save(student.state_dict(),\n", " os.path.join(TCFG.save_dir, f\"model_e{epoch+1:02d}.pt\"))\n", "\n", " # ── Final save ──\n", " torch.save(student.state_dict(),\n", " os.path.join(TCFG.save_dir, \"final_model.pt\"))\n", "\n", " # Save tokenizer info for standalone usage\n", " tokenizer.save_pretrained(os.path.join(TCFG.save_dir, \"tokenizer\"))\n", "\n", " with open(os.path.join(TCFG.save_dir, \"metrics.json\"), \"w\") as f:\n", " json.dump(all_metrics, f, indent=2, default=str)\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # FINAL EVALUATION\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"FINAL EVALUATION\")\n", " print(f\"{'='*65}\")\n", "\n", " # Load best\n", " student.load_state_dict(\n", " torch.load(os.path.join(TCFG.save_dir, \"best_model.pt\"),\n", " weights_only=True, map_location=DEVICE))\n", " student.eval()\n", "\n", " with torch.no_grad():\n", " val_embs = []\n", " for vi in range(0, TCFG.n_val, 512):\n", " vj = min(vi + 512, TCFG.n_val)\n", " ve = student(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(ve)\n", " val_emb = torch.cat(val_embs)\n", "\n", " # Retrieval metrics\n", " sim = val_emb @ val_targets.T\n", " labels = torch.arange(TCFG.n_val, device=DEVICE)\n", " r1 = (sim.argmax(1) == labels).float().mean().item()\n", " r5 = (sim.topk(5, dim=1).indices == labels.unsqueeze(1)).any(1).float().mean().item()\n", " r10 = (sim.topk(10, dim=1).indices == labels.unsqueeze(1)).any(1).float().mean().item()\n", "\n", " # Cosine stats\n", " cos_match = sim.diag().mean().item()\n", " cos_random = (sim.sum() - sim.diag().sum()).item() / (TCFG.n_val**2 - TCFG.n_val)\n", "\n", " # CV\n", " final_cv = cv_metric(val_emb)\n", "\n", " # Self-similarity (how well does student retrieve itself)\n", " self_sim = val_emb @ val_emb.T\n", " self_sim.fill_diagonal_(0)\n", "\n", " print(f\" Student → Consensus retrieval:\")\n", " print(f\" R@1: {r1:.4f}\")\n", " print(f\" R@5: {r5:.4f}\")\n", " print(f\" R@10: {r10:.4f}\")\n", " print(f\" Cosine similarity:\")\n", " print(f\" Matched pairs: {cos_match:.4f}\")\n", " print(f\" Random pairs: {cos_random:.4f}\")\n", " print(f\" Geometry:\")\n", " print(f\" CV: {final_cv:.4f} (target: {TCFG.cv_target})\")\n", " print(f\" Model size:\")\n", " print(f\" Parameters: {n_params:,}\")\n", " size_mb = sum(p.numel() * p.element_size() for p in student.parameters()) / 1e6\n", " print(f\" Size: {size_mb:.1f} MB\")\n", "\n", " # ── Standalone inference example ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"STANDALONE INFERENCE TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " test_captions = [\n", " \"A cat sitting on a windowsill watching birds outside\",\n", " \"A golden retriever playing fetch on the beach at sunset\",\n", " \"A still life painting with flowers and fruit on a table\",\n", " \"An aerial photograph of a city skyline at night\",\n", " \"A child riding a bicycle through autumn leaves in a park\",\n", " ]\n", "\n", " with torch.no_grad():\n", " tokens = tokenizer(test_captions, max_length=TCFG.max_len,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " embeddings = student(tokens[\"input_ids\"], tokens[\"attention_mask\"])\n", "\n", " # Pairwise similarity\n", " sim = embeddings @ embeddings.T\n", " print(f\"\\n Pairwise cosine similarity:\")\n", " for i in range(len(test_captions)):\n", " for j in range(i+1, len(test_captions)):\n", " print(f\" [{i}]↔[{j}]: {sim[i,j]:.3f} \"\n", " f\"({test_captions[i][:40]}... ↔ {test_captions[j][:40]}...)\")\n", "\n", " print(f\"\\n Saved to: {TCFG.save_dir}/\")\n", " print(f\" Files: best_model.pt, final_model.pt, tokenizer/, metrics.json\")\n", " print(f\"\\n Standalone usage:\")\n", " print(f\" model = CaptionEncoder(...)\")\n", " print(f\" model.load_state_dict(torch.load('best_model.pt'))\")\n", " print(f\" tokens = tokenizer(text, max_length=128, ...)\")\n", " print(f\" embedding = model(tokens.input_ids, tokens.attention_mask)\")\n", " print(f\" # → (B, 768) L2-normalized, consensus-aligned embedding\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xQ_BobJFaZkc", "outputId": "ca81f69b-e05f-47ba-80f2-c987bbd30fe6" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "DISTILLED CONSENSUS BERT\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating consensus targets from five-BERT cache...\n", " bert: torch.Size([20000, 768])\n", " modern: torch.Size([20000, 768])\n", " roberta: torch.Size([20000, 768])\n", " albert: torch.Size([20000, 768])\n", " distil: torch.Size([20000, 768])\n", " Aligned bert\n", " Aligned modern\n", " Aligned roberta\n", " Aligned albert\n", " Aligned distil\n", " Loaded consensus model (seed 42)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ " Generating targets: 100%|██████████| 40/40 [00:00<00:00, 296.05it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Consensus targets: torch.Size([20000, 768])\n", " Saved to /home/claude/five_berts_cache/consensus_targets.pt\n", "\n", " Tokenizer: bert-base-uncased (vocab=30522)\n", " Pre-tokenizing...\n", " Token lengths: mean=92 median=100 max=128 >128: 22.5%\n", " Train: 18000, Val: 2000\n", "\n", "=================================================================\n", "STUDENT MODEL\n", "=================================================================\n", " Architecture: 4 layers, 384-dim, 6 heads, 1536 FFN\n", " Output: 768-dim (matches consensus)\n", " Parameters: 19,312,512\n", "\n", "=================================================================\n", "TRAINING (20 epochs)\n", " Losses: InfoNCE + MSE + pentachoron CV (target=0.084)\n", "=================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_1529/1100325579.py:178: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E 1: 4s loss=4.4534 t_acc=0.163 t_cos=0.104 v_acc=0.236 v_cos=0.265 v_cv=0.230\n", " E 2: 4s loss=2.0709 t_acc=0.715 t_cos=0.340 v_acc=0.642 v_cos=0.422 v_cv=0.196\n", " E 3: 4s loss=1.1505 t_acc=0.908 t_cos=0.450 v_acc=0.833 v_cos=0.502 v_cv=0.169\n", " E 4: 4s loss=0.7151 t_acc=0.967 t_cos=0.513 v_acc=0.913 v_cos=0.552 v_cv=0.146\n", " E 5: 4s loss=0.4807 t_acc=0.986 t_cos=0.556 v_acc=0.932 v_cos=0.579 v_cv=0.133\n", " E 6: 4s loss=0.3423 t_acc=0.994 t_cos=0.588 v_acc=0.962 v_cos=0.604 v_cv=0.130\n", " E 7: 4s loss=0.2501 t_acc=0.997 t_cos=0.615 v_acc=0.970 v_cos=0.628 v_cv=0.127\n", " E 8: 4s loss=0.1849 t_acc=0.999 t_cos=0.638 v_acc=0.981 v_cos=0.646 v_cv=0.113\n", " E 9: 4s loss=0.1487 t_acc=1.000 t_cos=0.654 v_acc=0.985 v_cos=0.659 v_cv=0.109\n", " E10: 4s loss=0.1271 t_acc=0.999 t_cos=0.665 v_acc=0.982 v_cos=0.665 v_cv=0.098\n", " E11: 4s loss=0.1116 t_acc=1.000 t_cos=0.674 v_acc=0.987 v_cos=0.673 v_cv=0.103\n", " E12: 4s loss=0.1027 t_acc=1.000 t_cos=0.681 v_acc=0.985 v_cos=0.675 v_cv=0.109\n", " E13: 4s loss=0.0935 t_acc=1.000 t_cos=0.687 v_acc=0.988 v_cos=0.683 v_cv=0.105\n", " E14: 4s loss=0.0880 t_acc=1.000 t_cos=0.691 v_acc=0.988 v_cos=0.684 v_cv=0.100\n", " E15: 4s loss=0.0834 t_acc=1.000 t_cos=0.695 v_acc=0.989 v_cos=0.687 v_cv=0.112\n", " E16: 4s loss=0.0804 t_acc=1.000 t_cos=0.698 v_acc=0.989 v_cos=0.688 v_cv=0.102\n", " E17: 4s loss=0.0781 t_acc=1.000 t_cos=0.700 v_acc=0.989 v_cos=0.689 v_cv=0.099\n", " E18: 4s loss=0.0767 t_acc=1.000 t_cos=0.701 v_acc=0.989 v_cos=0.690 v_cv=0.123\n", " E19: 4s loss=0.0759 t_acc=1.000 t_cos=0.702 v_acc=0.989 v_cos=0.690 v_cv=0.103\n", " E20: 4s loss=0.0760 t_acc=1.000 t_cos=0.702 v_acc=0.989 v_cos=0.690 v_cv=0.102\n", "\n", "=================================================================\n", "FINAL EVALUATION\n", "=================================================================\n", " Student → Consensus retrieval:\n", " R@1: 0.9890\n", " R@5: 0.9985\n", " R@10: 1.0000\n", " Cosine similarity:\n", " Matched pairs: 0.6905\n", " Random pairs: 0.0005\n", " Geometry:\n", " CV: 0.1043 (target: 0.084)\n", " Model size:\n", " Parameters: 19,312,512\n", " Size: 77.3 MB\n", "\n", "=================================================================\n", "STANDALONE INFERENCE TEST\n", "=================================================================\n", "\n", " Pairwise cosine similarity:\n", " [0]↔[1]: 0.677 (A cat sitting on a windowsill watching b... ↔ A golden retriever playing fetch on the ...)\n", " [0]↔[2]: 0.404 (A cat sitting on a windowsill watching b... ↔ A still life painting with flowers and f...)\n", " [0]↔[3]: 0.421 (A cat sitting on a windowsill watching b... ↔ An aerial photograph of a city skyline a...)\n", " [0]↔[4]: 0.595 (A cat sitting on a windowsill watching b... ↔ A child riding a bicycle through autumn ...)\n", " [1]↔[2]: 0.354 (A golden retriever playing fetch on the ... ↔ A still life painting with flowers and f...)\n", " [1]↔[3]: 0.353 (A golden retriever playing fetch on the ... ↔ An aerial photograph of a city skyline a...)\n", " [1]↔[4]: 0.508 (A golden retriever playing fetch on the ... ↔ A child riding a bicycle through autumn ...)\n", " [2]↔[3]: 0.363 (A still life painting with flowers and f... ↔ An aerial photograph of a city skyline a...)\n", " [2]↔[4]: 0.357 (A still life painting with flowers and f... ↔ A child riding a bicycle through autumn ...)\n", " [3]↔[4]: 0.353 (An aerial photograph of a city skyline a... ↔ A child riding a bicycle through autumn ...)\n", "\n", " Saved to: /home/claude/distilled_consensus/\n", " Files: best_model.pt, final_model.pt, tokenizer/, metrics.json\n", "\n", " Standalone usage:\n", " model = CaptionEncoder(...)\n", " model.load_state_dict(torch.load('best_model.pt'))\n", " tokens = tokenizer(text, max_length=128, ...)\n", " embedding = model(tokens.input_ids, tokens.attention_mask)\n", " # → (B, 768) L2-normalized, consensus-aligned embedding\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# distill 2" ], "metadata": { "id": "GSm-HEucekY8" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# DISTILLED CONSENSUS BERT — 200K Scale\n", "#\n", "# Self-contained pipeline:\n", "# 1. Extract 5 BERT-family embeddings on 200K CC12M captions\n", "# 2. Whitened Procrustes alignment\n", "# 3. Generate consensus targets (centroid of aligned embeddings)\n", "# 4. Train small standalone transformer from scratch\n", "# 5. No expert models needed at inference\n", "# ============================================================================\n", "\n", "import math\n", "import os\n", "import time\n", "import json\n", "from dataclasses import dataclass\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "MODELS = [\n", " (\"google-bert/bert-base-uncased\", \"bert\"),\n", " (\"answerdotai/ModernBERT-base\", \"modern\"),\n", " (\"FacebookAI/roberta-base\", \"roberta\"),\n", " (\"albert/albert-base-v2\", \"albert\"),\n", " (\"distilbert/distilbert-base-uncased\", \"distil\"),\n", "]\n", "\n", "@dataclass\n", "class Config:\n", " # Data\n", " n_samples: int = 200000\n", " n_val: int = 5000\n", " min_caption_len: int = 50\n", " extract_batch: int = 128\n", " extract_max_len: int = 512\n", " cache_dir: str = \"/home/claude/consensus_200k\"\n", "\n", " # Student architecture\n", " d_model: int = 384\n", " n_heads: int = 6\n", " n_layers: int = 6\n", " d_ff: int = 1536\n", " max_len: int = 128\n", " output_dim: int = 768\n", " dropout: float = 0.1\n", "\n", " # Training\n", " epochs: int = 30\n", " batch_size: int = 256\n", " lr: float = 3e-4\n", " weight_decay: float = 0.01\n", " warmup_steps: int = 500\n", " grad_clip: float = 1.0\n", " seed: int = 42\n", "\n", " # Loss\n", " nce_weight: float = 1.0\n", " mse_weight: float = 1.0\n", " cv_weight: float = 0.1\n", " cv_target: float = 0.084\n", "\n", "CFG = Config()\n", "\n", "print(\"=\" * 65)\n", "print(\"DISTILLED CONSENSUS BERT — 200K Scale\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" Samples: {CFG.n_samples:,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACTION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def load_captions(n, min_len=50):\n", " from datasets import load_dataset\n", " print(f\"\\n Loading captions (n={n:,})...\")\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > min_len:\n", " captions.append(cap)\n", " if len(captions) >= n:\n", " break\n", " print(f\" Got {len(captions):,} captions\")\n", " return captions\n", "\n", "\n", "@torch.no_grad()\n", "def extract_one(model_name, short_name, captions, max_len, batch_size):\n", " from transformers import AutoModel, AutoTokenizer\n", " print(f\"\\n Extracting: {short_name} ({model_name})...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " dim = model.config.hidden_size\n", " n_params = sum(p.numel() for p in model.parameters())\n", " print(f\" dim={dim}, {n_params:,} params\")\n", "\n", " all_emb = []\n", " for i in tqdm(range(0, len(captions), batch_size), desc=f\" {short_name}\"):\n", " batch = captions[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " mask = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", "\n", " emb = torch.cat(all_emb)\n", " print(f\" Shape: {emb.shape}\")\n", " del model\n", " torch.cuda.empty_cache()\n", " return emb\n", "\n", "\n", "def extract_all():\n", " os.makedirs(CFG.cache_dir, exist_ok=True)\n", " caps_path = os.path.join(CFG.cache_dir, \"captions.json\")\n", "\n", " all_cached = all(\n", " os.path.exists(os.path.join(CFG.cache_dir, f\"{s}.pt\"))\n", " for _, s in MODELS)\n", "\n", " if all_cached and os.path.exists(caps_path):\n", " print(\"\\n Loading cached embeddings...\")\n", " embeds = {}\n", " for _, short in MODELS:\n", " embeds[short] = torch.load(\n", " os.path.join(CFG.cache_dir, f\"{short}.pt\"), weights_only=True)\n", " print(f\" {short}: {embeds[short].shape}\")\n", " with open(caps_path) as f:\n", " captions = json.load(f)\n", " return embeds, captions\n", "\n", " captions = load_captions(CFG.n_samples, CFG.min_caption_len)\n", "\n", " embeds = {}\n", " for model_name, short in MODELS:\n", " emb = extract_one(model_name, short, captions,\n", " CFG.extract_max_len, CFG.extract_batch)\n", " if emb.shape[1] != 768:\n", " if emb.shape[1] < 768:\n", " emb = F.pad(emb, (0, 768 - emb.shape[1]))\n", " else:\n", " emb = emb[:, :768]\n", " embeds[short] = emb\n", " torch.save(emb, os.path.join(CFG.cache_dir, f\"{short}.pt\"))\n", "\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", "\n", " return embeds, captions\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# WHITENED PROCRUSTES + CONSENSUS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float()\n", " T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True)\n", " t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean\n", " Tc = T - t_mean\n", " N_s = Sc.shape[0]\n", "\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", "\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", "\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", "\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", "\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", "\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]\n", " x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]\n", " return x\n", "\n", "\n", "def generate_consensus(embeds):\n", " \"\"\"Align all to bert space, take normalized centroid as target.\"\"\"\n", " print(f\"\\n{'='*65}\")\n", " print(\"WHITENED PROCRUSTES ALIGNMENT + CONSENSUS\")\n", " print(f\"{'='*65}\")\n", "\n", " ref_name = \"bert\"\n", " names = [s for _, s in MODELS]\n", " aligned = {}\n", "\n", " for name in names:\n", " info = procrustes_align(embeds[name], embeds[ref_name])\n", " aligned[name] = apply_align(embeds[name], info)\n", " label = \" (ref)\" if name == ref_name else \"\"\n", " print(f\" {name:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}{label}\")\n", "\n", " # Consensus = normalized centroid of all 5 aligned embeddings\n", " # This is what the five-BERT experiment proved: the centroid IS the consensus\n", " # to three decimal places regardless of seed. No learned model needed.\n", " centroid = sum(aligned[n] for n in names) / len(names)\n", " consensus = F.normalize(centroid, dim=-1)\n", "\n", " # Verify geometry\n", " N_check = min(5000, consensus.shape[0])\n", " for name in names:\n", " cos = F.cosine_similarity(\n", " consensus[:N_check], aligned[name][:N_check], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name:10s}): {cos:.4f}\")\n", "\n", " return consensus\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STUDENT MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=128, d_model=384,\n", " n_heads=6, n_layers=6, d_ff=1536, output_dim=768,\n", " dropout=0.1, pad_token_id=0):\n", " super().__init__()\n", " self.pad_token_id = pad_token_id\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff,\n", " dropout=dropout, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n", "\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model),\n", " nn.GELU(),\n", " nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim),\n", " )\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " B, L = input_ids.shape\n", " positions = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.token_emb(input_ids) + self.pos_emb(positions)\n", " x = self.emb_drop(self.emb_norm(x))\n", "\n", " if attention_mask is not None:\n", " kpm = ~attention_mask.bool()\n", " else:\n", " kpm = (input_ids == self.pad_token_id)\n", "\n", " x = self.encoder(x, src_key_padding_mask=kpm)\n", "\n", " if attention_mask is not None:\n", " mask = attention_mask.unsqueeze(-1).float()\n", " else:\n", " mask = (~kpm).unsqueeze(-1).float()\n", " pooled = (x * mask).sum(1) / mask.sum(1).clamp(min=1)\n", "\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.084, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train():\n", " torch.manual_seed(CFG.seed)\n", " torch.cuda.manual_seed_all(CFG.seed)\n", " np.random.seed(CFG.seed)\n", "\n", " # ── Extract + Align + Consensus ──\n", " embeds, captions = extract_all()\n", " consensus = generate_consensus(embeds)\n", "\n", " # Free the raw embeddings\n", " del embeds\n", " torch.cuda.empty_cache()\n", " import gc; gc.collect()\n", "\n", " # ── Tokenize ──\n", " from transformers import AutoTokenizer\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " print(f\"\\n Tokenizer: bert-base-uncased (vocab={tokenizer.vocab_size})\")\n", "\n", " print(\" Pre-tokenizing...\")\n", " # Tokenize in chunks to avoid memory issues\n", " all_ids, all_masks = [], []\n", " chunk = 50000\n", " for i in tqdm(range(0, len(captions), chunk), desc=\" Tokenizing\"):\n", " j = min(i + chunk, len(captions))\n", " tokens = tokenizer(captions[i:j], max_length=CFG.max_len,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\")\n", " all_ids.append(tokens[\"input_ids\"])\n", " all_masks.append(tokens[\"attention_mask\"])\n", "\n", " input_ids = torch.cat(all_ids)\n", " attention_mask = torch.cat(all_masks)\n", "\n", " real_lens = attention_mask.sum(1).float()\n", " print(f\" Token lengths: mean={real_lens.mean():.0f} \"\n", " f\"median={real_lens.median():.0f} \"\n", " f\">{CFG.max_len}: {(real_lens >= CFG.max_len).float().mean():.1%}\")\n", "\n", " # Split\n", " n_train = len(captions) - CFG.n_val\n", " print(f\" Train: {n_train:,}, Val: {CFG.n_val:,}\")\n", "\n", " # Move to GPU\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " # ── Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"STUDENT MODEL\")\n", " print(f\"{'='*65}\")\n", "\n", " student = CaptionEncoder(\n", " vocab_size=tokenizer.vocab_size,\n", " max_len=CFG.max_len,\n", " d_model=CFG.d_model,\n", " n_heads=CFG.n_heads,\n", " n_layers=CFG.n_layers,\n", " d_ff=CFG.d_ff,\n", " output_dim=CFG.output_dim,\n", " dropout=CFG.dropout,\n", " pad_token_id=tokenizer.pad_token_id,\n", " ).to(DEVICE)\n", "\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Architecture: {CFG.n_layers}L, {CFG.d_model}d, {CFG.n_heads}h, {CFG.d_ff} FFN\")\n", " print(f\" Output: {CFG.output_dim}-dim (consensus space)\")\n", " print(f\" Parameters: {n_params:,}\")\n", " size_mb = sum(p.numel() * p.element_size() for p in student.parameters()) / 1e6\n", " print(f\" Size: {size_mb:.1f} MB\")\n", "\n", " # ── Optimizer ──\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=CFG.lr,\n", " weight_decay=CFG.weight_decay)\n", " n_batches = n_train // CFG.batch_size\n", " total_steps = n_batches * CFG.epochs\n", " scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=CFG.warmup_steps),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - CFG.warmup_steps, 1),\n", " eta_min=1e-6)],\n", " milestones=[CFG.warmup_steps])\n", "\n", " os.makedirs(CFG.cache_dir, exist_ok=True)\n", " save_dir = os.path.join(CFG.cache_dir, \"student\")\n", " os.makedirs(save_dir, exist_ok=True)\n", "\n", " # ── Train ──\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({CFG.epochs} epochs, {n_batches} batches/epoch)\")\n", " print(f\"{'='*65}\")\n", "\n", " all_metrics = {\"config\": {k: str(v) for k, v in vars(CFG).items()}, \"epochs\": []}\n", " best_val_cos = 0.0\n", "\n", " for epoch in range(CFG.epochs):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " losses = {\"total\": 0, \"nce\": 0, \"mse\": 0}\n", " metrics = {\"acc\": 0, \"cos\": 0}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, CFG.batch_size):\n", " idx = perm[i:i+CFG.batch_size]\n", " if len(idx) < 8: continue\n", "\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", "\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=CFG.cv_target)\n", "\n", " loss = CFG.nce_weight * l_nce + CFG.mse_weight * l_mse + CFG.cv_weight * l_cv\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), CFG.grad_clip)\n", " optimizer.step()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", "\n", " losses[\"total\"] += loss.item()\n", " losses[\"nce\"] += l_nce.item()\n", " losses[\"mse\"] += l_mse.item()\n", " metrics[\"acc\"] += acc\n", " metrics[\"cos\"] += cos\n", " n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", "\n", " # Val\n", " student.eval()\n", " with torch.no_grad():\n", " val_embs = []\n", " for vi in range(0, CFG.n_val, 512):\n", " vj = min(vi + 512, CFG.n_val)\n", " ve = student(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(ve)\n", " val_emb = torch.cat(val_embs)\n", " _, val_acc = infonce(val_emb[:2000], val_targets[:2000])\n", " val_cos = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", " val_cv = cv_metric(val_emb[:2000])\n", "\n", " summary = {\n", " \"epoch\": epoch + 1, \"elapsed\": elapsed,\n", " \"loss\": losses[\"total\"] / d,\n", " \"train_acc\": metrics[\"acc\"] / d,\n", " \"train_cos\": metrics[\"cos\"] / d,\n", " \"val_acc\": val_acc, \"val_cos\": val_cos, \"val_cv\": val_cv,\n", " }\n", " all_metrics[\"epochs\"].append(summary)\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s \"\n", " f\"loss={summary['loss']:.4f} \"\n", " f\"t_acc={summary['train_acc']:.3f} t_cos={summary['train_cos']:.3f} \"\n", " f\"v_acc={summary['val_acc']:.3f} v_cos={summary['val_cos']:.3f} \"\n", " f\"v_cv={summary['val_cv']:.3f}\")\n", "\n", " if val_cos > best_val_cos:\n", " best_val_cos = val_cos\n", " torch.save(student.state_dict(), os.path.join(save_dir, \"best_model.pt\"))\n", "\n", " if (epoch + 1) % 10 == 0:\n", " torch.save(student.state_dict(),\n", " os.path.join(save_dir, f\"model_e{epoch+1:02d}.pt\"))\n", "\n", " # Final save\n", " torch.save(student.state_dict(), os.path.join(save_dir, \"final_model.pt\"))\n", " tokenizer.save_pretrained(os.path.join(save_dir, \"tokenizer\"))\n", " with open(os.path.join(save_dir, \"metrics.json\"), \"w\") as f:\n", " json.dump(all_metrics, f, indent=2, default=str)\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # FINAL EVAL\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"FINAL EVALUATION\")\n", " print(f\"{'='*65}\")\n", "\n", " student.load_state_dict(\n", " torch.load(os.path.join(save_dir, \"best_model.pt\"),\n", " weights_only=True, map_location=DEVICE))\n", " student.eval()\n", "\n", " with torch.no_grad():\n", " val_embs = []\n", " for vi in range(0, CFG.n_val, 512):\n", " vj = min(vi + 512, CFG.n_val)\n", " ve = student(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(ve)\n", " val_emb = torch.cat(val_embs)\n", "\n", " # Retrieval (on 2K subset for memory)\n", " sub = min(2000, CFG.n_val)\n", " sim = val_emb[:sub] @ val_targets[:sub].T\n", " labels = torch.arange(sub, device=DEVICE)\n", " r1 = (sim.argmax(1) == labels).float().mean().item()\n", " r5 = (sim.topk(5, dim=1).indices == labels.unsqueeze(1)).any(1).float().mean().item()\n", " r10 = (sim.topk(10, dim=1).indices == labels.unsqueeze(1)).any(1).float().mean().item()\n", "\n", " cos_match = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", " final_cv = cv_metric(val_emb[:2000])\n", "\n", " print(f\" Retrieval (student → consensus):\")\n", " print(f\" R@1: {r1:.4f}\")\n", " print(f\" R@5: {r5:.4f}\")\n", " print(f\" R@10: {r10:.4f}\")\n", " print(f\" Cosine: {cos_match:.4f}\")\n", " print(f\" CV: {final_cv:.4f} (target: {CFG.cv_target})\")\n", " print(f\" Model: {n_params:,} params, {size_mb:.1f} MB\")\n", "\n", " # Standalone test\n", " print(f\"\\n Standalone similarity test:\")\n", " test = [\n", " \"A cat sitting on a windowsill watching birds\",\n", " \"A golden retriever playing fetch on the beach\",\n", " \"A still life painting with flowers and fruit\",\n", " \"An aerial photograph of a city skyline at night\",\n", " \"A child riding a bicycle through autumn leaves\",\n", " ]\n", " with torch.no_grad():\n", " tok = tokenizer(test, max_length=CFG.max_len, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " embs = student(tok[\"input_ids\"], tok[\"attention_mask\"])\n", " sim = embs @ embs.T\n", " for i in range(len(test)):\n", " for j in range(i+1, len(test)):\n", " print(f\" [{i}]↔[{j}]: {sim[i,j]:.3f} \"\n", " f\"({test[i][:35]}↔{test[j][:35]})\")\n", "\n", " print(f\"\\n Saved to: {save_dir}/\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0eaf1b9b3faf4564b2ea751d3292a62b", "61f98e3de9f54cd8bb6ac5abf94c4547", "e81f9655321c435f898aaf1d2bef667c", "ff6d3c3b5f6c45a58746baf241ab8f69", "6311dfcc5ede419cbebd064cb42af75a", "5f3e8530131045459d831a4cd75d752d", "80acec4b777947bf9481d1b6292178c0", "1a6e19bfe5244157a110d80615352827", "96d62c3895ec40449c75d8527dc6f1e5", "3e71024a339a4938a94205fcbb4ba530", "2038b2fbe7d846178ecf6cf083ca55de", "abdd244173644b379041034854e908fa", "122a0e0816f945ceb1942cd3d10f51f6", "6ef64c471dc94f609440fb4c4ed0f9e4", "449a39840fac45a9bf33b016a4ecde66", "af961b31ee0e4cabac70dd4e61cd2f05", "e372742ba260499b875618da8b0b62df", "a3d8b3f6224a4f5e85a1b1a8fc116cc0", "3273f54a6fcc4e82aee577b2b508b493", "631ea49dbaa3495688f16e0b27c90610", "beed45db486d40dbb70fd5a6713fcd5d", "4a2611a199da4fef9dafd0c3934e02fe", "defad155b30b4076ae5431e8b299a148", "cac0d30d1ba848f28e2722a7baddf6fe", "38b121d084cc40e58727c89299acfef3", "bbe4c87a57804f3d9332abc541c938ed", "7754907597864c7386d8234500b1df3b", "34c8e8c9f27f4384b33a50f37f0dd97c", "65d3b06a14c74e9f944194dbe682d070", "8ff7e87e23e048ce90ca973a803874e9", "07d12ebd42d1426091b26eeb77a17d97", "d77868ac7580404490ca0f80ec607cc3", "d666624772a6477d90dce56414e97ac9", "8eb0acb8d0174d89ad3869e17c73468c", "ba39be331d814cb4ac763e3f9fde1032", "132de8e2ddd541269a810f44cc6fb971", "e4ce3e29132a45649817df4f7e8341e2", "3bf5704b4d6942909ad163b3b91318af", "87dbb92120d84e418a9ba3770b6ddb73", "e4eda51e8e614607901352ff01958332", "e0234180e8644ba99c472de64827ce69", "99482875446a40d488ded2b1522820f9", "a177a905339d494a9938dee8e6446983", "c5ed7bba1e6a4cf18e5bc3bda01bcada", "380a95ce1d17446fb7a8370f95e2d62d", "17ba56553c1545dc8899a7ff580e3b4a", "43e22d4a71a74918be6c8c4a6dccd928", "96ddc7a8e7f4482aa863aae9194bc2fa", "698be3985bc14d93bdb4f2d8ad7114f9", "07154f164e574d4ea174183bab680de6", "b943e03d5d094af2abf688177557d1b3", "5daca214c00c40a08aaa0ccb44406d93", "d473e9f0063341a4871d5c507d012fe7", "4a39b4a3ddb5468ebeb0f595732d681f", "92a8707d70614dada558e2cc8c90b1d7" ] }, "id": "lVZLPcTlel2K", "outputId": "d4a6f306-5d25-4173-a938-bccb6607cf18" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "DISTILLED CONSENSUS BERT — 200K Scale\n", "=================================================================\n", " Device: cuda\n", " Samples: 200,000\n", "\n", " Loading captions (n=200,000)...\n", " Got 200,000 captions\n", "\n", " Extracting: bert (google-bert/bert-base-uncased)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00128: 22.8%\n", " Train: 195,000, Val: 5,000\n", "\n", "=================================================================\n", "STUDENT MODEL\n", "=================================================================\n", " Architecture: 6L, 384d, 6h, 1536 FFN\n", " Output: 768-dim (consensus space)\n", " Parameters: 22,861,440\n", " Size: 91.4 MB\n", "\n", "=================================================================\n", "TRAINING (30 epochs, 761 batches/epoch)\n", "=================================================================\n", " E 1: 61s loss=1.3769 t_acc=0.787 t_cos=0.517 v_acc=0.979 v_cos=0.702 v_cv=0.141\n", " E 2: 61s loss=0.1657 t_acc=0.998 t_cos=0.722 v_acc=0.994 v_cos=0.745 v_cv=0.112\n", " E 3: 61s loss=0.0995 t_acc=1.000 t_cos=0.752 v_acc=0.997 v_cos=0.766 v_cv=0.108\n", " E 4: 61s loss=0.0778 t_acc=1.000 t_cos=0.766 v_acc=0.997 v_cos=0.771 v_cv=0.106\n", " E 5: 61s loss=0.0658 t_acc=1.000 t_cos=0.776 v_acc=0.999 v_cos=0.776 v_cv=0.103\n", " E 6: 61s loss=0.0580 t_acc=1.000 t_cos=0.782 v_acc=0.998 v_cos=0.789 v_cv=0.113\n", " E 7: 61s loss=0.0523 t_acc=1.000 t_cos=0.789 v_acc=0.999 v_cos=0.793 v_cv=0.088\n", " E 8: 61s loss=0.0479 t_acc=1.000 t_cos=0.793 v_acc=1.000 v_cos=0.795 v_cv=0.101\n", " E 9: 61s loss=0.0449 t_acc=1.000 t_cos=0.797 v_acc=1.000 v_cos=0.798 v_cv=0.090\n", " E10: 61s loss=0.0419 t_acc=1.000 t_cos=0.801 v_acc=1.000 v_cos=0.801 v_cv=0.084\n", " E11: 61s loss=0.0399 t_acc=1.000 t_cos=0.805 v_acc=1.000 v_cos=0.807 v_cv=0.094\n", " E12: 61s loss=0.0379 t_acc=1.000 t_cos=0.809 v_acc=1.000 v_cos=0.810 v_cv=0.099\n", " E13: 61s loss=0.0360 t_acc=1.000 t_cos=0.812 v_acc=1.000 v_cos=0.813 v_cv=0.099\n", " E14: 61s loss=0.0345 t_acc=1.000 t_cos=0.814 v_acc=1.000 v_cos=0.814 v_cv=0.092\n", " E15: 61s loss=0.0332 t_acc=1.000 t_cos=0.817 v_acc=1.000 v_cos=0.815 v_cv=0.088\n", " E16: 61s loss=0.0321 t_acc=1.000 t_cos=0.819 v_acc=1.000 v_cos=0.817 v_cv=0.102\n", " E17: 61s loss=0.0310 t_acc=1.000 t_cos=0.821 v_acc=1.000 v_cos=0.820 v_cv=0.092\n", " E18: 61s loss=0.0300 t_acc=1.000 t_cos=0.824 v_acc=1.000 v_cos=0.822 v_cv=0.091\n", " E19: 61s loss=0.0292 t_acc=1.000 t_cos=0.827 v_acc=1.000 v_cos=0.822 v_cv=0.086\n", " E20: 61s loss=0.0284 t_acc=1.000 t_cos=0.828 v_acc=1.000 v_cos=0.827 v_cv=0.091\n", " E21: 61s loss=0.0277 t_acc=1.000 t_cos=0.830 v_acc=1.000 v_cos=0.826 v_cv=0.087\n", " E22: 61s loss=0.0272 t_acc=1.000 t_cos=0.831 v_acc=1.000 v_cos=0.829 v_cv=0.093\n", " E23: 61s loss=0.0264 t_acc=1.000 t_cos=0.833 v_acc=1.000 v_cos=0.830 v_cv=0.081\n", " E24: 61s loss=0.0259 t_acc=1.000 t_cos=0.836 v_acc=1.000 v_cos=0.829 v_cv=0.090\n", " E25: 61s loss=0.0257 t_acc=1.000 t_cos=0.836 v_acc=1.000 v_cos=0.830 v_cv=0.082\n", " E26: 61s loss=0.0251 t_acc=1.000 t_cos=0.838 v_acc=1.000 v_cos=0.831 v_cv=0.086\n", " E27: 61s loss=0.0251 t_acc=1.000 t_cos=0.838 v_acc=1.000 v_cos=0.832 v_cv=0.090\n", " E28: 61s loss=0.0248 t_acc=1.000 t_cos=0.839 v_acc=1.000 v_cos=0.833 v_cv=0.091\n", " E29: 61s loss=0.0247 t_acc=1.000 t_cos=0.840 v_acc=1.000 v_cos=0.833 v_cv=0.085\n", " E30: 61s loss=0.0249 t_acc=1.000 t_cos=0.840 v_acc=1.000 v_cos=0.833 v_cv=0.090\n", "\n", "=================================================================\n", "FINAL EVALUATION\n", "=================================================================\n", " Retrieval (student → consensus):\n", " R@1: 1.0000\n", " R@5: 1.0000\n", " R@10: 1.0000\n", " Cosine: 0.8333\n", " CV: 0.0894 (target: 0.084)\n", " Model: 22,861,440 params, 91.4 MB\n", "\n", " Standalone similarity test:\n", " [0]↔[1]: 0.702 (A cat sitting on a windowsill watch↔A golden retriever playing fetch on)\n", " [0]↔[2]: 0.356 (A cat sitting on a windowsill watch↔A still life painting with flowers )\n", " [0]↔[3]: 0.421 (A cat sitting on a windowsill watch↔An aerial photograph of a city skyl)\n", " [0]↔[4]: 0.445 (A cat sitting on a windowsill watch↔A child riding a bicycle through au)\n", " [1]↔[2]: 0.340 (A golden retriever playing fetch on↔A still life painting with flowers )\n", " [1]↔[3]: 0.407 (A golden retriever playing fetch on↔An aerial photograph of a city skyl)\n", " [1]↔[4]: 0.396 (A golden retriever playing fetch on↔A child riding a bicycle through au)\n", " [2]↔[3]: 0.381 (A still life painting with flowers ↔An aerial photograph of a city skyl)\n", " [2]↔[4]: 0.471 (A still life painting with flowers ↔A child riding a bicycle through au)\n", " [3]↔[4]: 0.382 (An aerial photograph of a city skyl↔A child riding a bicycle through au)\n", "\n", " Saved to: /home/claude/consensus_200k/student/\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# BENCHMARK: Distilled Consensus Student vs Individual BERTs\n", "#\n", "# Tests:\n", "# 1. STS-B (Semantic Textual Similarity Benchmark) — Spearman correlation\n", "# 2. SICK-R (Sentences Involving Compositional Knowledge) — Spearman\n", "# 3. Retrieval precision on held-out consensus targets\n", "#\n", "# Compares:\n", "# - Distilled student (19-23M params, no pretrained weights)\n", "# - BERT-base-uncased (110M params)\n", "# - ModernBERT-base (149M params)\n", "# - RoBERTa-base (125M params)\n", "# - ALBERT-base-v2 (12M params)\n", "# - DistilBERT-base (66M params)\n", "#\n", "# All models evaluated on mean-pooled embeddings → cosine similarity\n", "# ============================================================================\n", "\n", "import os\n", "import json\n", "import torch\n", "import torch.nn.functional as F\n", "import numpy as np\n", "from scipy.stats import spearmanr, pearsonr\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"BENCHMARK: Consensus Student vs Individual BERTs\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD BENCHMARKS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def load_stsb():\n", " \"\"\"Load STS-B test set.\"\"\"\n", " from datasets import load_dataset\n", " ds = load_dataset(\"mteb/stsbenchmark-sts\", split=\"test\")\n", " pairs = []\n", " for row in ds:\n", " pairs.append({\n", " \"sent1\": row[\"sentence1\"],\n", " \"sent2\": row[\"sentence2\"],\n", " \"score\": row[\"score\"],\n", " })\n", " print(f\" STS-B test: {len(pairs)} pairs, scores {min(p['score'] for p in pairs):.1f}-{max(p['score'] for p in pairs):.1f}\")\n", " return pairs\n", "\n", "\n", "def load_sick():\n", " \"\"\"Load SICK-R test set.\"\"\"\n", " from datasets import load_dataset\n", " ds = load_dataset(\"mteb/sickr-sts\", split=\"test\")\n", " pairs = []\n", " for row in ds:\n", " pairs.append({\n", " \"sent1\": row[\"sentence1\"],\n", " \"sent2\": row[\"sentence2\"],\n", " \"score\": row[\"score\"],\n", " })\n", " print(f\" SICK-R test: {len(pairs)} pairs, scores {min(p['score'] for p in pairs):.1f}-{max(p['score'] for p in pairs):.1f}\")\n", " return pairs\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ENCODE FUNCTIONS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def encode_with_hf_model(model, tokenizer, texts, batch_size=128, max_len=128):\n", " \"\"\"Mean-pooled encoding from any HF model.\"\"\"\n", " all_emb = []\n", " for i in range(0, len(texts), batch_size):\n", " batch = texts[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " mask = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " all_emb.append(F.normalize(pooled, dim=-1).cpu())\n", " return torch.cat(all_emb)\n", "\n", "\n", "@torch.no_grad()\n", "def encode_with_student(student, tokenizer, texts, batch_size=128, max_len=128):\n", " \"\"\"Encode using the distilled student.\"\"\"\n", " all_emb = []\n", " for i in range(0, len(texts), batch_size):\n", " batch = texts[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " emb = student(inputs[\"input_ids\"], inputs[\"attention_mask\"])\n", " all_emb.append(emb.cpu())\n", " return torch.cat(all_emb)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVALUATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def eval_sts(pairs, emb1, emb2):\n", " \"\"\"Compute Spearman and Pearson correlation on STS-style task.\"\"\"\n", " cosines = F.cosine_similarity(emb1, emb2, dim=-1).numpy()\n", " gold = np.array([p[\"score\"] for p in pairs])\n", " spearman = spearmanr(cosines, gold).statistic\n", " pearson = pearsonr(cosines, gold).statistic\n", " return {\n", " \"spearman\": float(spearman),\n", " \"pearson\": float(pearson),\n", " \"cos_mean\": float(cosines.mean()),\n", " \"cos_std\": float(cosines.std()),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STUDENT MODEL (must match training architecture)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "import torch.nn as nn\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=128, d_model=384,\n", " n_heads=6, n_layers=6, d_ff=1536, output_dim=768,\n", " dropout=0.1, pad_token_id=0):\n", " super().__init__()\n", " self.pad_token_id = pad_token_id\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff,\n", " dropout=dropout, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(),\n", " nn.LayerNorm(d_model), nn.Linear(d_model, output_dim))\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " B, L = input_ids.shape\n", " positions = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.token_emb(input_ids) + self.pos_emb(positions)\n", " x = self.emb_drop(self.emb_norm(x))\n", " if attention_mask is not None:\n", " kpm = ~attention_mask.bool()\n", " else:\n", " kpm = (input_ids == self.pad_token_id)\n", " x = self.encoder(x, src_key_padding_mask=kpm)\n", " if attention_mask is not None:\n", " mask = attention_mask.unsqueeze(-1).float()\n", " else:\n", " mask = (~kpm).unsqueeze(-1).float()\n", " pooled = (x * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run_benchmarks():\n", " from transformers import AutoModel, AutoTokenizer\n", " import gc\n", "\n", " # ── Load benchmarks ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING BENCHMARKS\")\n", " print(f\"{'='*65}\")\n", "\n", " stsb = load_stsb()\n", " sick = load_sick()\n", "\n", " stsb_s1 = [p[\"sent1\"] for p in stsb]\n", " stsb_s2 = [p[\"sent2\"] for p in stsb]\n", " sick_s1 = [p[\"sent1\"] for p in sick]\n", " sick_s2 = [p[\"sent2\"] for p in sick]\n", "\n", " results = {}\n", "\n", " # ── Evaluate student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"EVALUATING: Distilled Consensus Student\")\n", " print(f\"{'='*65}\")\n", "\n", " student_tok = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", "\n", " # Try loading from 200K path first, then 20K\n", " student = None\n", " for save_dir in [\"/home/claude/consensus_200k/student\",\n", " \"/home/claude/distilled_consensus\"]:\n", " for ckpt in [\"best_model.pt\", \"final_model.pt\"]:\n", " p = os.path.join(save_dir, ckpt)\n", " if os.path.exists(p):\n", " student = CaptionEncoder(\n", " vocab_size=student_tok.vocab_size,\n", " max_len=128, d_model=384, n_heads=6, n_layers=6,\n", " d_ff=1536, output_dim=768, dropout=0.0,\n", " pad_token_id=student_tok.pad_token_id).to(DEVICE)\n", " student.load_state_dict(\n", " torch.load(p, weights_only=True, map_location=DEVICE))\n", " student.eval()\n", " n_params = sum(pp.numel() for pp in student.parameters())\n", " print(f\" Loaded: {p}\")\n", " print(f\" Parameters: {n_params:,}\")\n", " break\n", " if student is not None:\n", " break\n", "\n", " if student is None:\n", " print(\" ERROR: No student checkpoint found!\")\n", " return\n", "\n", " # Encode\n", " print(\" Encoding STS-B...\")\n", " s_stsb1 = encode_with_student(student, student_tok, stsb_s1)\n", " s_stsb2 = encode_with_student(student, student_tok, stsb_s2)\n", " print(\" Encoding SICK-R...\")\n", " s_sick1 = encode_with_student(student, student_tok, sick_s1)\n", " s_sick2 = encode_with_student(student, student_tok, sick_s2)\n", "\n", " r_stsb = eval_sts(stsb, s_stsb1, s_stsb2)\n", " r_sick = eval_sts(sick, s_sick1, s_sick2)\n", " results[\"student\"] = {\"stsb\": r_stsb, \"sick\": r_sick, \"params\": n_params}\n", " print(f\" STS-B: spearman={r_stsb['spearman']:.4f} pearson={r_stsb['pearson']:.4f}\")\n", " print(f\" SICK-R: spearman={r_sick['spearman']:.4f} pearson={r_sick['pearson']:.4f}\")\n", "\n", " del student\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " # ── Evaluate individual BERTs ──\n", " bert_models = [\n", " (\"google-bert/bert-base-uncased\", \"bert-base\"),\n", " (\"answerdotai/ModernBERT-base\", \"modern-bert\"),\n", " (\"FacebookAI/roberta-base\", \"roberta\"),\n", " (\"albert/albert-base-v2\", \"albert\"),\n", " (\"distilbert/distilbert-base-uncased\", \"distilbert\"),\n", " ]\n", "\n", " for model_name, short_name in bert_models:\n", " print(f\"\\n{'='*65}\")\n", " print(f\"EVALUATING: {short_name} ({model_name})\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " n_p = sum(p.numel() for p in model.parameters())\n", " print(f\" Parameters: {n_p:,}\")\n", "\n", " print(\" Encoding STS-B...\")\n", " e_stsb1 = encode_with_hf_model(model, tokenizer, stsb_s1)\n", " e_stsb2 = encode_with_hf_model(model, tokenizer, stsb_s2)\n", " print(\" Encoding SICK-R...\")\n", " e_sick1 = encode_with_hf_model(model, tokenizer, sick_s1)\n", " e_sick2 = encode_with_hf_model(model, tokenizer, sick_s2)\n", "\n", " r_stsb = eval_sts(stsb, e_stsb1, e_stsb2)\n", " r_sick = eval_sts(sick, e_sick1, e_sick2)\n", " results[short_name] = {\"stsb\": r_stsb, \"sick\": r_sick, \"params\": n_p}\n", " print(f\" STS-B: spearman={r_stsb['spearman']:.4f} pearson={r_stsb['pearson']:.4f}\")\n", " print(f\" SICK-R: spearman={r_sick['spearman']:.4f} pearson={r_sick['pearson']:.4f}\")\n", "\n", " del model\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # SUMMARY\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\"\\n {'Model':<20} {'Params':>12} {'STS-B ρ':>10} {'SICK-R ρ':>10}\")\n", " print(f\" {'-'*52}\")\n", "\n", " # Sort by STS-B spearman\n", " sorted_results = sorted(results.items(),\n", " key=lambda x: x[1][\"stsb\"][\"spearman\"], reverse=True)\n", " for name, r in sorted_results:\n", " marker = \" ★\" if name == \"student\" else \"\"\n", " print(f\" {name:<20} {r['params']:>10,} \"\n", " f\"{r['stsb']['spearman']:>10.4f} {r['sick']['spearman']:>10.4f}{marker}\")\n", "\n", " # Student vs best individual\n", " student_stsb = results[\"student\"][\"stsb\"][\"spearman\"]\n", " best_name = max((n for n in results if n != \"student\"),\n", " key=lambda n: results[n][\"stsb\"][\"spearman\"])\n", " best_stsb = results[best_name][\"stsb\"][\"spearman\"]\n", " best_params = results[best_name][\"params\"]\n", " student_params = results[\"student\"][\"params\"]\n", "\n", " print(f\"\\n Student STS-B: {student_stsb:.4f} ({student_params:,} params)\")\n", " print(f\" Best teacher: {best_stsb:.4f} ({best_name}, {best_params:,} params)\")\n", " print(f\" Gap: {student_stsb - best_stsb:+.4f}\")\n", " print(f\" Param ratio: {best_params / student_params:.1f}×\")\n", "\n", " # Save\n", " save_path = \"/home/claude/benchmark_results.json\"\n", " with open(save_path, \"w\") as f:\n", " json.dump(results, f, indent=2, default=str)\n", " print(f\"\\n Saved to {save_path}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run_benchmarks()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "445251c2934740f2bf149cd2e7848887", "94548dbad7fd4c8386087683768b5644", "8654b6b67b294522a6c6005820745747", "2da39e52fb8d463bae47fdb295fd95c2", "fab849e9e4d34fdcb47fb6f5b37ce8b7", "e71d4fe04ba14e41b3d34012299b33f0", "24ea33a9dd494980902f2d2d2d3c2f2e", "f993dc3a96564c188d3cade339ffaf7e", "4b7bde4ca12d4d3c99d1fd7a3efe1dc9", "6ce03d413a0849a78aa4683cbd3333d6", "8fe8bf9594dd451d94f976ae77c6c309", "b77c9510ee784585aa2856f8c4e0787f", "c7e1fe16b5614caf859d30fa44db4be9", "39773bab82b241fbb2999844c9812516", "e88a3f9b46a64d2f9f535cd9eb713c70", "dcfd19088e03463990d21307614ccd6a", "441822419f114790abbdea3fe0f87d0d", "4197c1a8df1d45999669aba6e088fe83", "2da61cc4d3d24578911ad10a79fd98d0", "cd89f0c06e7c408aa15df2668eb89c2b", "5914712138f840d4830a08e1286818bd", "8b01634426a646a58a4bb4fff708ea9d", "a3ec1bca46b54d608ed1cec3e9b86a77", "e373cfc9515b414893098c943e9b3544", "7922153899924061b12e434ffb84d37a", "feb0891d370a4ab0b617e4ff7c9fe7b8", "44cb8dc7bbce44c49baf4d5239b6bb25", "33165e1fd85f4d33882db63d0d59cae4", "059a83e42aab417a8e3b451d06ecbfe8", "c9963fa14058479b83ba8fd96addd7d9", "7a9f11ff114e4093a2b03366c2273164", "ce79ca6d9e864136be126a1474c8d389", "35d1c561a6c145d183f24daf78c71453", "b1f74d52b7594a8db5b1ede3cb901715", "3e897c0e4f804c1bac03d892bbfe5022", "2277e5a2cd184f928daeb73c0acbde94", "43cc92e2c6d245d097ab3d15f3e77f2e", "e818f395a11d4fdeaa03a54bddabc91c", "6f644b61c77b480a868b391ee704d51c", "1b35574690a3408eb527b43c6775bbd4", "90cc3d74316f4c59973a067b035e3d20", "0cd89d5a5abc4654965285254ad615da", "358642b7ed2a43868cc8b702ce0efc6c", "adf03a58a46749b7bae27df5f5b018ed", "79f9be2aa6964b42b4eafe00c3856d35", "8ffcc3129027449b827a5f3edfad279d", "0a05d9c36747437e9a1962213fdf328e", "c65bc22e7c654ac2ba342f548ba26344", 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"b08b134a4a654ce1b58bade830939e43", "db5e9dbf211f482fb729859345411acb", "cfc335914cbf43b084f2884bfcd16b08", "6ddcd1a0106b43898e9bfe457b737b50", "2a85e704dc5042a783295611891d3673", "e4d4c1dae6874d54b99f92f89bca7318", "f5b69ff3b2a14f4e8b44b012a72fae5b", "ada3af63f20e46a59ab3d496f477610d", "24c77bb630ab4c159a75647090b618de", "20550b2196d64a129e9d45107395ae25", "c6a0987845784bdb8ae012cf8884769a", "76fd271429ac46ab8418a8ac6ffc0cb9", "764233414e36462f95f94269c63d710b", "189700333aa048728934be7b5d828645", "30b4705e2bf045f9beac5635eada4e82", "a414d6711a94476b995ab71e5fb5fccc", "689bee8e0e964a4a853af9a3e00bb2f0", "a52a7381cfdb45d59f2c8d464ecfe4bf", "60aa1ad490ec453abac816adbecad28e", "0ccfd37c64df4fc2bc9c2019acb4f3fb", "38b077de9ea74eb6befdca50b10f4a8e", "221b0c6322624ee8817b9c1d35315b73", "66c3da07e60e4ea4a86739687596620c", "dccd8c3c10a34ff9a6de3870ee0645f8", "e0a5762e6fdd486a897fa94109e3b988", "d9389ca3df834d2f9a64e8b0bb308a29", "aeece63f05df4ce78953275473abdbad", "0b4bc34ed08e4a24b28c280cc3063646", "bdd1e56b56cb4079a5c6395c825daf72", "c667166f08174334b209b0be75da36f8", "7eab67765b894fb1883704e73df5b6d0", "ab9914baaa1a4481b26c702d112b2d5a", "c3f947b3292c457f91f8591b35b2564a", "046cf8d05fe444589e58d255dbf5db8f", "f5c305fa31ce443c940a9f16c1ef68ae", "508568909bae475b9c29be12d70b0ccd", "b7bd72832268451f80619571231406e6", "4e7a916f70064f319c23928c44c1b3de", "e7e7ee73b9e743f8973fb3ba937d78dc", "4462cb5698df46e18cdfccba1f7538f9", "44b68e0e5de24b5f80043a9429588b66", "1c38a351b71e49f580822113040568cc", "a11f1eb907c74990a36234b1dc0970ea", "e3b66d14d74048e9ae0d215148121b6b", "bd5723c7ac0b4211b32d76b8b775f14f", "9092d99755a347ac8a45ae016e819ee7", "7f618a7728e948168acd1d46a016022f", "e9317d14d03d47b58112946964fccba4", "c3d63defe838429892e07e3fe4743525", "3c01fb2c7fe947838b39254ef68096bb", "bde8ae428f394b2e97af1a09d1171612", "960f59cc25f94d38b47214d9958d9eb0", "eb6b2d96790243ee9bc6bd581f1c0d7b", "afb1d381e40e4bc7b96bb3e05bd5b01e", "79a83cd59ee04936b90d2106330af650", "e9c2f24d106a43bbaa576b910f7b10a2", "8ebfbf0823b44c668ec5db96e177a7f2", "925475ff7a5a456690873c59a688d1cf", "35e71e1fecbb4c5db7d4848056cb273e", "44ca9db110fa465bb6aea334fae9fb73", "eb399846160a4e5a9ac1fb44f01c554a", "01e5df033e78448c8fea2a10c601eed5", "8b73d9c5065741cf930497f238fec29c", "d2f8cc33ff5b4ed9b183c7a80bfbf17c", "8ab7372bef074e47bc6620bb94949a19", "5f725231dd86474aa60ac6fde76508b7", "93a148c2568943d89dde0d3a9ad9aece", "2733ae5d3ae049f0bff7929ee3f256fa", "41235a1f501c4280a21a96b6a0415a35", "fe8ac8b38d5949bf957b0789371af8eb", "1f6c77432fb74833abecca6146cb2ccf", "022ed8d5166142b4a5a97d0df26e8048", "bfb3b36c2947429cb7474628ed89e573", "fe6758b05193460bac4f73e05a6c4f5f", "ceb23d6435844a3eb4beaab9e103eca6", "256955cf07f04930b76d2c7bcef293ac", "e4afa7b6702b486c8531f2f5c0e1821e", "ebde16de147546f0918466c31897aef5", "d5d72d42cddc48a287b7076758543c95", "6dbac12dff1d4d82a8abf2c295eb7788", "a4967e28ec7b4203947aff3d3f98ab5f", "1ab19f3149f341fdb27fb6b2fcd5cbf1", "b267a673008045d88549e9248bfd3a64", "19cfe604fa5c4d3db43f0a32e8a35c42", "ab66728ec8eb47b8923f85535a34a8f5", "a581ce9c6d684f5d9dca44b18f62a034", "7baa5ad8efe44571a33999119a9a4b5d", "ecbb8b12316c42efb6dec051b55bc1fa", "ca2a86d355644fdcb204a44ced3d4ec7", "d2fa9d75def7493b8878733b386e076c" ] }, "id": "zWMH2My6rNUw", "outputId": "bc119d38-897f-4ba2-bf9c-eea93134528b" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "BENCHMARK: Consensus Student vs Individual BERTs\n", "=================================================================\n", "\n", "=================================================================\n", "LOADING BENCHMARKS\n", "=================================================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "445251c2934740f2bf149cd2e7848887" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "train.jsonl.gz: 0%| | 0.00/278k [00:00 0:\n", " vols.append(v)\n", " if len(vols) < 10:\n", " return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA: Caption loading + 5-BERT extraction\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def load_captions(n, min_len=50):\n", " from datasets import load_dataset\n", " print(f\"\\n Loading captions (n={n:,})...\")\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > min_len:\n", " captions.append(cap)\n", " if len(captions) >= n:\n", " break\n", " print(f\" Got {len(captions):,} captions\")\n", " return captions\n", "\n", "\n", "@torch.no_grad()\n", "def extract_one(model_name, short_name, captions, max_len, batch_size):\n", " \"\"\"Extract mean-pooled embeddings from a single HF model.\"\"\"\n", " from transformers import AutoModel, AutoTokenizer\n", " print(f\"\\n Extracting: {short_name} ({model_name}, max_len={max_len})...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " dim = model.config.hidden_size\n", " n_params = sum(p.numel() for p in model.parameters())\n", " print(f\" dim={dim}, {n_params:,} params\")\n", "\n", " all_emb = []\n", " for i in tqdm(range(0, len(captions), batch_size), desc=f\" {short_name}\"):\n", " batch = captions[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " mask = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", "\n", " emb = torch.cat(all_emb)\n", " print(f\" Shape: {emb.shape}\")\n", " del model, tokenizer\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", " return emb\n", "\n", "\n", "def extract_all():\n", " \"\"\"Extract embeddings from all 5 models. Caches to disk.\"\"\"\n", " os.makedirs(CFG.cache_dir, exist_ok=True)\n", " caps_path = os.path.join(CFG.cache_dir, \"captions.json\")\n", "\n", " all_cached = all(\n", " os.path.exists(os.path.join(CFG.cache_dir, f\"{s}.pt\"))\n", " for _, s, _ in MODELS)\n", "\n", " if all_cached:\n", " print(\"\\n Loading cached embeddings...\")\n", " embeds = {}\n", " for _, short, _ in MODELS:\n", " embeds[short] = torch.load(\n", " os.path.join(CFG.cache_dir, f\"{short}.pt\"), weights_only=True)\n", " print(f\" {short}: {embeds[short].shape}\")\n", "\n", " # Load or regenerate captions\n", " if os.path.exists(caps_path):\n", " with open(caps_path) as f:\n", " captions = json.load(f)\n", " print(f\" Captions loaded: {len(captions):,}\")\n", " else:\n", " print(\" captions.json missing, regenerating...\")\n", " captions = load_captions(CFG.n_samples, CFG.min_caption_len)\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", "\n", " # Trim to smallest common size\n", " n = min(len(captions), min(e.shape[0] for e in embeds.values()))\n", " captions = captions[:n]\n", " embeds = {k: v[:n] for k, v in embeds.items()}\n", " print(f\" Using {n:,} samples\")\n", " return embeds, captions\n", "\n", " captions = load_captions(CFG.n_samples, CFG.min_caption_len)\n", "\n", " embeds = {}\n", " for model_name, short, model_max_len in MODELS:\n", " out_path = os.path.join(CFG.cache_dir, f\"{short}.pt\")\n", " if os.path.exists(out_path):\n", " print(f\"\\n {short}: cached, loading...\")\n", " embeds[short] = torch.load(out_path, weights_only=True)\n", " print(f\" Shape: {embeds[short].shape}\")\n", " continue\n", " emb = extract_one(model_name, short, captions,\n", " model_max_len, CFG.extract_batch)\n", " # Ensure 768-dim\n", " if emb.shape[1] != 768:\n", " if emb.shape[1] < 768:\n", " emb = F.pad(emb, (0, 768 - emb.shape[1]))\n", " else:\n", " emb = emb[:, :768]\n", " embeds[short] = emb\n", " torch.save(emb, os.path.join(CFG.cache_dir, f\"{short}.pt\"))\n", "\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", "\n", " return embeds, captions\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# WHITENED PROCRUSTES ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " \"\"\"Covariance matrix inverse square root for whitening.\"\"\"\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " \"\"\"Whitened Procrustes: center → whiten → normalize → SVD rotate.\"\"\"\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float()\n", " T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True)\n", " t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean\n", " Tc = T - t_mean\n", " N_s = Sc.shape[0]\n", "\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", "\n", " # Whiten\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", "\n", " # SVD rotation\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", "\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " t_unwhiten = torch.linalg.pinv(t_whiten)\n", "\n", " return {\n", " \"rotation\": R,\n", " \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": t_unwhiten,\n", " \"cos_before\": cos_before,\n", " \"cos_after\": cos_after,\n", " }\n", "\n", "\n", "def apply_align(emb, a):\n", " \"\"\"Apply whitened Procrustes: center → whiten → rotate → unwhiten.\"\"\"\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]\n", " x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]\n", " return x\n", "\n", "\n", "def generate_consensus(embeds):\n", " \"\"\"\n", " Align all 5 models to shared space, return normalized centroid.\n", " The five-BERT pentachoron experiment proved this centroid is a\n", " geometric constant (identical to 3 decimal places across 5 seeds).\n", " No learned model needed.\n", " \"\"\"\n", " print(f\"\\n{'='*65}\")\n", " print(\"WHITENED PROCRUSTES ALIGNMENT + CONSENSUS\")\n", " print(f\"{'='*65}\")\n", "\n", " ref_name = \"bert\"\n", " names = [s for _, s, _ in MODELS]\n", " aligned = {}\n", "\n", " for name in names:\n", " info = procrustes_align(embeds[name], embeds[ref_name])\n", " aligned[name] = apply_align(embeds[name], info)\n", " label = \" (ref)\" if name == ref_name else \"\"\n", " print(f\" {name:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}{label}\")\n", "\n", " # Consensus = normalized centroid\n", " centroid = sum(aligned[n] for n in names) / len(names)\n", " consensus = F.normalize(centroid, dim=-1)\n", "\n", " # Verify\n", " N_check = min(5000, consensus.shape[0])\n", " for name in names:\n", " cos = F.cosine_similarity(\n", " consensus[:N_check], aligned[name][:N_check], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name:10s}): {cos:.4f}\")\n", "\n", " return consensus\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train():\n", " torch.manual_seed(CFG.seed)\n", " torch.cuda.manual_seed_all(CFG.seed)\n", " np.random.seed(CFG.seed)\n", "\n", " # ── Extract + Align + Consensus ──\n", " embeds, captions = extract_all()\n", " consensus = generate_consensus(embeds)\n", "\n", " # Free raw embeddings\n", " del embeds\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " # ── Tokenize ──\n", " from transformers import AutoTokenizer\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " print(f\"\\n Tokenizer: bert-base-uncased (vocab={tokenizer.vocab_size})\")\n", "\n", " print(\" Pre-tokenizing...\")\n", " all_ids, all_masks = [], []\n", " chunk = 50000\n", " for i in tqdm(range(0, len(captions), chunk), desc=\" Tokenizing\"):\n", " j = min(i + chunk, len(captions))\n", " tokens = tokenizer(captions[i:j], max_length=CFG.tokenize_len,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\")\n", " all_ids.append(tokens[\"input_ids\"])\n", " all_masks.append(tokens[\"attention_mask\"])\n", "\n", " input_ids = torch.cat(all_ids)\n", " attention_mask = torch.cat(all_masks)\n", "\n", " real_lens = attention_mask.sum(1).float()\n", " print(f\" Token lengths: mean={real_lens.mean():.0f} \"\n", " f\"median={real_lens.median():.0f} \"\n", " f\">{CFG.tokenize_len}: {(real_lens >= CFG.tokenize_len).float().mean():.1%}\")\n", " print(f\" Padded to: {CFG.tokenize_len} (model supports up to {CFG.max_len})\")\n", "\n", " # Split\n", " n_train = len(captions) - CFG.n_val\n", " print(f\" Train: {n_train:,}, Val: {CFG.n_val:,}\")\n", "\n", " # Move to GPU\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " # ── Build student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"STUDENT MODEL: CaptionEncoder\")\n", " print(f\"{'='*65}\")\n", "\n", " student = CaptionEncoder(\n", " vocab_size=tokenizer.vocab_size,\n", " max_len=CFG.max_len,\n", " d_model=CFG.d_model,\n", " n_heads=CFG.n_heads,\n", " n_layers=CFG.n_layers,\n", " d_ff=CFG.d_ff,\n", " output_dim=CFG.output_dim,\n", " dropout=CFG.dropout,\n", " pad_token_id=tokenizer.pad_token_id,\n", " ).to(DEVICE)\n", "\n", " n_params = sum(p.numel() for p in student.parameters())\n", " size_mb = sum(p.numel() * p.element_size() for p in student.parameters()) / 1e6\n", " print(f\" Architecture: {CFG.n_layers}L, {CFG.d_model}d, {CFG.n_heads}h, {CFG.d_ff} FFN\")\n", " print(f\" Position capacity: {CFG.max_len}\")\n", " print(f\" Output: {CFG.output_dim}-dim (consensus space)\")\n", " print(f\" Parameters: {n_params:,}\")\n", " print(f\" Size: {size_mb:.1f} MB\")\n", "\n", " # ── Warm-start from previous checkpoint if available ──\n", " warm_started = False\n", " for prev_dir in [\"/home/claude/consensus_200k/student\",\n", " \"/home/claude/distilled_consensus\"]:\n", " prev_ckpt = os.path.join(prev_dir, \"best_model.pt\")\n", " if os.path.exists(prev_ckpt):\n", " print(f\"\\n Warm-starting from: {prev_ckpt}\")\n", " prev_state = torch.load(prev_ckpt, weights_only=True, map_location=DEVICE)\n", " current_state = student.state_dict()\n", "\n", " loaded, extended, skipped = 0, 0, 0\n", " for name, param in prev_state.items():\n", " if name not in current_state:\n", " skipped += 1\n", " continue\n", " if param.shape == current_state[name].shape:\n", " current_state[name] = param\n", " loaded += 1\n", " elif \"pos_emb\" in name and param.shape[0] < current_state[name].shape[0]:\n", " # Extend position embeddings: copy old, init new\n", " old_len = param.shape[0]\n", " current_state[name][:old_len] = param\n", " nn.init.normal_(current_state[name][old_len:], std=0.02)\n", " extended += 1\n", " print(f\" Extended {name}: {param.shape[0]}→{current_state[name].shape[0]}\")\n", " else:\n", " skipped += 1\n", " print(f\" Skipped {name}: {param.shape}→{current_state[name].shape}\")\n", "\n", " student.load_state_dict(current_state)\n", " print(f\" Loaded: {loaded}, Extended: {extended}, Skipped: {skipped}\")\n", " warm_started = True\n", " break\n", "\n", " if not warm_started:\n", " print(\"\\n Training from scratch (no previous checkpoint found)\")\n", "\n", " # ── Optimizer + Scheduler ──\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=CFG.lr,\n", " weight_decay=CFG.weight_decay)\n", " n_batches = n_train // CFG.batch_size\n", " total_steps = n_batches * CFG.epochs\n", " scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=CFG.warmup_steps),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - CFG.warmup_steps, 1),\n", " eta_min=1e-6)],\n", " milestones=[CFG.warmup_steps])\n", "\n", " save_dir = os.path.join(CFG.cache_dir, \"student\")\n", " os.makedirs(save_dir, exist_ok=True)\n", "\n", " # ── Training loop ──\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({CFG.epochs} epochs, {n_batches} batches/epoch)\")\n", " print(f\" Losses: InfoNCE + MSE + pentachoron CV (target={CFG.cv_target})\")\n", " print(f\"{'='*65}\")\n", "\n", " all_metrics = {\n", " \"config\": {k: str(v) for k, v in vars(CFG).items()},\n", " \"warm_started\": warm_started,\n", " \"epochs\": [],\n", " }\n", " best_val_cos = 0.0\n", "\n", " for epoch in range(CFG.epochs):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " losses = {\"total\": 0, \"nce\": 0, \"mse\": 0}\n", " metrics = {\"acc\": 0, \"cos\": 0}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, CFG.batch_size):\n", " idx = perm[i:i+CFG.batch_size]\n", " if len(idx) < 8:\n", " continue\n", "\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", "\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=CFG.cv_target)\n", "\n", " loss = (CFG.nce_weight * l_nce +\n", " CFG.mse_weight * l_mse +\n", " CFG.cv_weight * l_cv)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), CFG.grad_clip)\n", " optimizer.step()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", "\n", " losses[\"total\"] += loss.item()\n", " losses[\"nce\"] += l_nce.item()\n", " losses[\"mse\"] += l_mse.item()\n", " metrics[\"acc\"] += acc\n", " metrics[\"cos\"] += cos\n", " n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", "\n", " # Validation\n", " student.eval()\n", " with torch.no_grad():\n", " val_embs = []\n", " for vi in range(0, CFG.n_val, 512):\n", " vj = min(vi + 512, CFG.n_val)\n", " ve = student(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(ve)\n", " val_emb = torch.cat(val_embs)\n", " _, val_acc = infonce(val_emb[:2000], val_targets[:2000])\n", " val_cos = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", " val_cv = cv_metric(val_emb[:2000])\n", "\n", " summary = {\n", " \"epoch\": epoch + 1, \"elapsed\": elapsed,\n", " \"loss\": losses[\"total\"] / d,\n", " \"train_acc\": metrics[\"acc\"] / d,\n", " \"train_cos\": metrics[\"cos\"] / d,\n", " \"val_acc\": val_acc, \"val_cos\": val_cos, \"val_cv\": val_cv,\n", " }\n", " all_metrics[\"epochs\"].append(summary)\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s \"\n", " f\"loss={summary['loss']:.4f} \"\n", " f\"t_acc={summary['train_acc']:.3f} t_cos={summary['train_cos']:.3f} \"\n", " f\"v_acc={summary['val_acc']:.3f} v_cos={summary['val_cos']:.3f} \"\n", " f\"v_cv={summary['val_cv']:.3f}\")\n", "\n", " # Save best\n", " if val_cos > best_val_cos:\n", " best_val_cos = val_cos\n", " torch.save(student.state_dict(), os.path.join(save_dir, \"best_model.pt\"))\n", "\n", " # Periodic save\n", " if (epoch + 1) % 10 == 0:\n", " torch.save(student.state_dict(),\n", " os.path.join(save_dir, f\"model_e{epoch+1:02d}.pt\"))\n", "\n", " # Final save\n", " torch.save(student.state_dict(), os.path.join(save_dir, \"final_model.pt\"))\n", " tokenizer.save_pretrained(os.path.join(save_dir, \"tokenizer\"))\n", " with open(os.path.join(save_dir, \"metrics.json\"), \"w\") as f:\n", " json.dump(all_metrics, f, indent=2, default=str)\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # FINAL EVALUATION\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"FINAL EVALUATION\")\n", " print(f\"{'='*65}\")\n", "\n", " student.load_state_dict(\n", " torch.load(os.path.join(save_dir, \"best_model.pt\"),\n", " weights_only=True, map_location=DEVICE))\n", " student.eval()\n", "\n", " with torch.no_grad():\n", " val_embs = []\n", " for vi in range(0, CFG.n_val, 512):\n", " vj = min(vi + 512, CFG.n_val)\n", " ve = student(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(ve)\n", " val_emb = torch.cat(val_embs)\n", "\n", " # Retrieval (on 2K subset)\n", " sub = min(2000, CFG.n_val)\n", " sim = val_emb[:sub] @ val_targets[:sub].T\n", " labels = torch.arange(sub, device=DEVICE)\n", " r1 = (sim.argmax(1) == labels).float().mean().item()\n", " r5 = (sim.topk(5, dim=1).indices == labels.unsqueeze(1)).any(1).float().mean().item()\n", " r10 = (sim.topk(10, dim=1).indices == labels.unsqueeze(1)).any(1).float().mean().item()\n", "\n", " cos_match = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", " final_cv = cv_metric(val_emb[:2000])\n", "\n", " print(f\" Retrieval (student → consensus):\")\n", " print(f\" R@1: {r1:.4f}\")\n", " print(f\" R@5: {r5:.4f}\")\n", " print(f\" R@10: {r10:.4f}\")\n", " print(f\" Cosine: {cos_match:.4f}\")\n", " print(f\" CV: {final_cv:.4f} (target: {CFG.cv_target})\")\n", " print(f\" Model: {n_params:,} params, {size_mb:.1f} MB\")\n", "\n", " # Standalone inference test\n", " print(f\"\\n Standalone similarity test:\")\n", " test = [\n", " \"A cat sitting on a windowsill watching birds outside\",\n", " \"A golden retriever playing fetch on the beach at sunset\",\n", " \"A still life painting with flowers and fruit on a table\",\n", " \"An aerial photograph of a city skyline at night\",\n", " \"A child riding a bicycle through autumn leaves in a park\",\n", " ]\n", " with torch.no_grad():\n", " tok = tokenizer(test, max_length=CFG.tokenize_len, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " embs = student(tok[\"input_ids\"], tok[\"attention_mask\"])\n", " sim = embs @ embs.T\n", " for i in range(len(test)):\n", " for j in range(i+1, len(test)):\n", " print(f\" [{i}]↔[{j}]: {sim[i,j]:.3f} \"\n", " f\"({test[i][:35]}↔{test[j][:35]})\")\n", "\n", " print(f\"\\n Saved to: {save_dir}/\")\n", " print(f\" Files: best_model.pt, final_model.pt, tokenizer/, metrics.json\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "3dcbb662b30f47cea1f426e9dc400ccd", "95e7bd361fd64d5da9eba70f738098f1", "aed55b8a33774489858846e8d91df294", "cb2850b0ed5c446da78ae62c66f31ab8", "177c8cace36648309828ff022da377c6", "18644eff9b28496f8272ea19fc3b7321", "f0d53e94e0754085b05a2385bda50e10", "407aa645322c43ebb807b1042381e460", "27987162e66c4fd5be03e00881c1767c", "1502c7cc58e84ee1a9f52e75a9b9b756", "b41d4555acca4842bcbcb68fedf14b82", "d80a44e073ac4e4d8a080144d253d7fe", "5e99b7b4abd448f6b52092186dbaa05e", "37f6bb1b4c384556b7b21667904d38ba", "0ca39f5cffca45e9b49e99c5547ff5b6", "08ff8a06c91646d699bb822e2128e6c4", "3361780a16294b8aadaf017d28360fd5", "8038f4a50f4340489f7c367f33a9dc88", "16f31db48da24f1ea6b6707475a0ba76", "d8e24943261740ef9b010859cc3fcbc1", "e72a0689679f4bddbfaad6387d290520", "b9dd65320fb24523b8bc0159e0f22eac", "d3e519965795424c8e03db777192988f", "b2178870751745ae8b2ff59efb912a6a", "596bca946934466ebac67b58bb55a2c6", "7239062a47784c0fb07e98e430e69c6c", "4ce5209809b34b5595c16501acaf36e2", "a4038ba6cc2745e9817d5442adb12713", "e0dec83560634bd7bac5bbfcaecb4857", "4261972c71334de188c80f166b653f15", "10da6bb464ca419eb17496580bf7d88f", "7b8ed631bda04a3dbfaeea4870a2a4b1", "9e729736efbd42218c3775334ae2238e", "db8fb5f7d5d641e0a63c27883875fbb8", "359bf897b2404f4ca3b1250d8d060c8b", "21233148c885454d9d1ccc5467b0877c", "b8a4c7d2168743028374e094fdba3778", "e001c3cb8a1a44cca95d5d191749e596", "49fb49ff55c14127b11e942667fddc5a", "251a783db5f74b50a7b32970c85fac1a", "bb23409acd204b52b03879633a2a3f81", "684d665814904e2ba054607b97a57b55", "ff5545a913c140ff96f42e3334942bdc", "a780ba2ef7834be6882edccace4cbe3b" ] }, "id": "N0WAE-pitGyM", "outputId": "6ad26487-f437-4aba-a20f-2ae0a7b8675f" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "DISTILLED CONSENSUS BERT — 500K / 8192 pos\n", "=================================================================\n", " Device: cuda\n", " Samples: 500,000\n", " Student: 6L 384d → 768d\n", " Pos capacity: 8192, Tokenize pad: 512\n", "\n", " Loading captions (n=500,000)...\n", " Got 500,000 captions\n", "\n", " bert: cached, loading...\n", " Shape: torch.Size([500000, 768])\n", "\n", " Extracting: modern (answerdotai/ModernBERT-base, max_len=8192)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/134 [00:00512: 0.0%\n", " Padded to: 512 (model supports up to 8192)\n", " Train: 495,000, Val: 5,000\n", "\n", "=================================================================\n", "STUDENT MODEL: CaptionEncoder\n", "=================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_49962/1732017288.py:120: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Architecture: 6L, 384d, 6h, 1536 FFN\n", " Position capacity: 8192\n", " Output: 768-dim (consensus space)\n", " Parameters: 25,958,016\n", " Size: 103.8 MB\n", "\n", " Warm-starting from: /home/claude/consensus_200k/student/best_model.pt\n", " Extended pos_emb.weight: 128→8192\n", " Loaded: 81, Extended: 1, Skipped: 0\n", "\n", "=================================================================\n", "TRAINING (30 epochs, 3867 batches/epoch)\n", " Losses: InfoNCE + MSE + pentachoron CV (target=0.084)\n", "=================================================================\n", " E 1: 689s loss=0.0197 t_acc=1.000 t_cos=0.804 v_acc=1.000 v_cos=0.803 v_cv=0.104\n", " E 2: 688s loss=0.0188 t_acc=1.000 t_cos=0.807 v_acc=1.000 v_cos=0.810 v_cv=0.085\n", " E 3: 688s loss=0.0176 t_acc=1.000 t_cos=0.811 v_acc=1.000 v_cos=0.820 v_cv=0.103\n", " E 4: 689s loss=0.0166 t_acc=1.000 t_cos=0.815 v_acc=1.000 v_cos=0.825 v_cv=0.084\n", " E 5: 689s loss=0.0159 t_acc=1.000 t_cos=0.819 v_acc=1.000 v_cos=0.819 v_cv=0.086\n", " E 6: 689s loss=0.0153 t_acc=1.000 t_cos=0.821 v_acc=1.000 v_cos=0.821 v_cv=0.095\n", " E 7: 688s loss=0.0148 t_acc=1.000 t_cos=0.824 v_acc=1.000 v_cos=0.820 v_cv=0.091\n", " E 8: 689s loss=0.0143 t_acc=1.000 t_cos=0.827 v_acc=1.000 v_cos=0.834 v_cv=0.088\n", " E 9: 688s loss=0.0139 t_acc=1.000 t_cos=0.829 v_acc=1.000 v_cos=0.829 v_cv=0.088\n", " E10: 689s loss=0.0136 t_acc=1.000 t_cos=0.831 v_acc=1.000 v_cos=0.829 v_cv=0.087\n", " E11: 689s loss=0.0133 t_acc=1.000 t_cos=0.833 v_acc=1.000 v_cos=0.836 v_cv=0.082\n", " E12: 689s loss=0.0129 t_acc=1.000 t_cos=0.835 v_acc=1.000 v_cos=0.838 v_cv=0.084\n", " E13: 688s loss=0.0126 t_acc=1.000 t_cos=0.837 v_acc=1.000 v_cos=0.842 v_cv=0.083\n", " E14: 689s loss=0.0123 t_acc=1.000 t_cos=0.839 v_acc=1.000 v_cos=0.842 v_cv=0.081\n", " E15: 688s loss=0.0121 t_acc=1.000 t_cos=0.842 v_acc=1.000 v_cos=0.840 v_cv=0.078\n", " E16: 689s loss=0.0118 t_acc=1.000 t_cos=0.843 v_acc=1.000 v_cos=0.843 v_cv=0.086\n", " E17: 689s loss=0.0116 t_acc=1.000 t_cos=0.846 v_acc=1.000 v_cos=0.845 v_cv=0.086\n", " E18: 689s loss=0.0114 t_acc=1.000 t_cos=0.847 v_acc=1.000 v_cos=0.848 v_cv=0.087\n", " E19: 688s loss=0.0111 t_acc=1.000 t_cos=0.849 v_acc=1.000 v_cos=0.849 v_cv=0.082\n", " E20: 690s loss=0.0110 t_acc=1.000 t_cos=0.851 v_acc=1.000 v_cos=0.849 v_cv=0.078\n", " E21: 689s loss=0.0108 t_acc=1.000 t_cos=0.853 v_acc=1.000 v_cos=0.855 v_cv=0.087\n", " E22: 689s loss=0.0106 t_acc=1.000 t_cos=0.855 v_acc=1.000 v_cos=0.856 v_cv=0.083\n", " E23: 689s loss=0.0104 t_acc=1.000 t_cos=0.857 v_acc=1.000 v_cos=0.855 v_cv=0.078\n", " E24: 688s loss=0.0102 t_acc=1.000 t_cos=0.858 v_acc=1.000 v_cos=0.857 v_cv=0.093\n", " E25: 689s loss=0.0101 t_acc=1.000 t_cos=0.860 v_acc=1.000 v_cos=0.859 v_cv=0.092\n", " E26: 689s loss=0.0100 t_acc=1.000 t_cos=0.861 v_acc=1.000 v_cos=0.860 v_cv=0.079\n", " E27: 689s loss=0.0099 t_acc=1.000 t_cos=0.863 v_acc=1.000 v_cos=0.862 v_cv=0.084\n", " E28: 688s loss=0.0099 t_acc=1.000 t_cos=0.863 v_acc=1.000 v_cos=0.862 v_cv=0.091\n", " E29: 688s loss=0.0099 t_acc=1.000 t_cos=0.863 v_acc=1.000 v_cos=0.862 v_cv=0.081\n", " E30: 689s loss=0.0099 t_acc=1.000 t_cos=0.863 v_acc=1.000 v_cos=0.862 v_cv=0.082\n", "\n", "=================================================================\n", "FINAL EVALUATION\n", "=================================================================\n", " Retrieval (student → consensus):\n", " R@1: 1.0000\n", " R@5: 1.0000\n", " R@10: 1.0000\n", " Cosine: 0.8621\n", " CV: 0.0767 (target: 0.084)\n", " Model: 25,958,016 params, 103.8 MB\n", "\n", " Standalone similarity test:\n", " [0]↔[1]: 0.623 (A cat sitting on a windowsill watch↔A golden retriever playing fetch on)\n", " [0]↔[2]: 0.429 (A cat sitting on a windowsill watch↔A still life painting with flowers )\n", " [0]↔[3]: 0.481 (A cat sitting on a windowsill watch↔An aerial photograph of a city skyl)\n", " [0]↔[4]: 0.478 (A cat sitting on a windowsill watch↔A child riding a bicycle through au)\n", " [1]↔[2]: 0.315 (A golden retriever playing fetch on↔A still life painting with flowers )\n", " [1]↔[3]: 0.548 (A golden retriever playing fetch on↔An aerial photograph of a city skyl)\n", " [1]↔[4]: 0.522 (A golden retriever playing fetch on↔A child riding a bicycle through au)\n", " [2]↔[3]: 0.437 (A still life painting with flowers ↔An aerial photograph of a city skyl)\n", " [2]↔[4]: 0.349 (A still life painting with flowers ↔A child riding a bicycle through au)\n", " [3]↔[4]: 0.460 (An aerial photograph of a city skyl↔A child riding a bicycle through au)\n", "\n", " Saved to: /home/claude/consensus_500k/student/\n", " Files: best_model.pt, final_model.pt, tokenizer/, metrics.json\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# UPLOAD: Distilled Consensus Student to HuggingFace\n", "# Repo: AbstractPhil/geolip-consensus-distilled\n", "# ============================================================================\n", "\n", "import os\n", "import json\n", "import tempfile\n", "import torch\n", "from huggingface_hub import HfApi, create_repo\n", "\n", "REPO_ID = \"AbstractPhil/geolip-captionbert-8192\"\n", "\n", "# Find the best checkpoint\n", "SAVE_DIRS = [\n", " \"/home/claude/consensus_500k/student\",\n", " \"/home/claude/consensus_200k/student\",\n", " \"/home/claude/distilled_consensus\",\n", "]\n", "\n", "save_dir = None\n", "for d in SAVE_DIRS:\n", " if os.path.exists(os.path.join(d, \"best_model.pt\")):\n", " save_dir = d\n", " break\n", "\n", "if save_dir is None:\n", " raise FileNotFoundError(\"No student checkpoint found!\")\n", "\n", "print(f\" Source: {save_dir}\")\n", "\n", "api = HfApi()\n", "try:\n", " create_repo(REPO_ID, repo_type=\"model\", exist_ok=True)\n", "except Exception as e:\n", " print(f\" Repo: {e}\")\n", "print(f\" Repo: https://huggingface.co/{REPO_ID}\")\n", "\n", "\n", "# ── 1. Model weights ──\n", "for ckpt in [\"best_model.pt\", \"final_model.pt\"]:\n", " p = os.path.join(save_dir, ckpt)\n", " if os.path.exists(p):\n", " size_mb = os.path.getsize(p) / 1e6\n", " api.upload_file(path_or_fileobj=p,\n", " path_in_repo=ckpt, repo_id=REPO_ID)\n", " print(f\"✓ {ckpt} ({size_mb:.1f} MB)\")\n", "\n", "# Epoch checkpoints\n", "for f in sorted(os.listdir(save_dir)):\n", " if f.startswith(\"model_e\") and f.endswith(\".pt\"):\n", " p = os.path.join(save_dir, f)\n", " api.upload_file(path_or_fileobj=p,\n", " path_in_repo=f\"checkpoints/{f}\", repo_id=REPO_ID)\n", " print(f\" ✓ checkpoints/{f}\")\n", "\n", "\n", "# ── 2. Tokenizer ──\n", "tok_dir = os.path.join(save_dir, \"tokenizer\")\n", "if os.path.exists(tok_dir):\n", " api.upload_folder(folder_path=tok_dir,\n", " path_in_repo=\"tokenizer\",\n", " repo_id=REPO_ID)\n", " print(\"✓ tokenizer/\")\n", "\n", "\n", "# ── 3. Metrics ──\n", "metrics_path = os.path.join(save_dir, \"metrics.json\")\n", "metrics = {}\n", "if os.path.exists(metrics_path):\n", " api.upload_file(path_or_fileobj=metrics_path,\n", " path_in_repo=\"metrics.json\", repo_id=REPO_ID)\n", " with open(metrics_path) as f:\n", " metrics = json.load(f)\n", " print(\"✓ metrics.json\")\n", "\n", "\n", "# ── 4. Model code (standalone, no external deps) ──\n", "model_code = '''# ============================================================================\n", "# CaptionEncoder: Standalone Consensus-Distilled Caption Embedding Model\n", "#\n", "# Produces 768-dim L2-normalized embeddings in geometric consensus space.\n", "# Trained via distillation from 5-BERT pentachoron consensus.\n", "# No expert models needed at inference.\n", "#\n", "# Usage:\n", "# from caption_encoder import CaptionEncoder\n", "# model = CaptionEncoder()\n", "# model.load_state_dict(torch.load(\"best_model.pt\"))\n", "# # tokenize with bert-base-uncased tokenizer\n", "# embedding = model(input_ids, attention_mask) # (B, 768) L2-normalized\n", "# ============================================================================\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "\n", "class CaptionEncoder(nn.Module):\n", " \"\"\"\n", " Standalone transformer caption encoder.\n", " No pretrained weights required. Trained via geometric consensus distillation.\n", "\n", " The embedding space is the geometric intersection of 5 BERT-family models:\n", " BERT-base, ModernBERT-base, RoBERTa-base, ALBERT-base-v2, DistilBERT-base.\n", " Aligned via whitened Procrustes rotation. Regularized by pentachoron CV.\n", "\n", " At inference: bert-base-uncased tokenizer + this model.\n", " Output: (B, 768) L2-normalized embedding in consensus space.\n", " \"\"\"\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=384,\n", " n_heads=6, n_layers=6, d_ff=1536, output_dim=768,\n", " dropout=0.1, pad_token_id=0):\n", " super().__init__()\n", " self.pad_token_id = pad_token_id\n", " self.d_model = d_model\n", " self.max_len = max_len\n", "\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff,\n", " dropout=dropout, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n", "\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model),\n", " nn.GELU(),\n", " nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim),\n", " )\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " B, L = input_ids.shape\n", " positions = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", "\n", " x = self.token_emb(input_ids) + self.pos_emb(positions)\n", " x = self.emb_drop(self.emb_norm(x))\n", "\n", " if attention_mask is not None:\n", " kpm = ~attention_mask.bool()\n", " else:\n", " kpm = (input_ids == self.pad_token_id)\n", "\n", " x = self.encoder(x, src_key_padding_mask=kpm)\n", "\n", " if attention_mask is not None:\n", " mask = attention_mask.unsqueeze(-1).float()\n", " else:\n", " mask = (~kpm).unsqueeze(-1).float()\n", " pooled = (x * mask).sum(1) / mask.sum(1).clamp(min=1)\n", "\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "'''\n", "\n", "with tempfile.NamedTemporaryFile(mode=\"w\", suffix=\".py\", delete=False) as f:\n", " f.write(model_code)\n", " tmp = f.name\n", "api.upload_file(path_or_fileobj=tmp, path_in_repo=\"caption_encoder.py\", repo_id=REPO_ID)\n", "os.unlink(tmp)\n", "print(\"✓ caption_encoder.py\")\n", "\n", "\n", "# ── 5. Config ──\n", "config = {\n", " \"model_type\": \"caption_encoder\",\n", " \"architectures\": [\"CaptionEncoder\"],\n", " \"vocab_size\": 30522,\n", " \"max_position_embeddings\": 8192,\n", " \"hidden_size\": 384,\n", " \"num_attention_heads\": 6,\n", " \"num_hidden_layers\": 6,\n", " \"intermediate_size\": 1536,\n", " \"output_dim\": 768,\n", " \"hidden_dropout_prob\": 0.1,\n", " \"pad_token_id\": 0,\n", " \"tokenizer\": \"google-bert/bert-base-uncased\",\n", " \"consensus_models\": [\n", " \"google-bert/bert-base-uncased\",\n", " \"answerdotai/ModernBERT-base\",\n", " \"FacebookAI/roberta-base\",\n", " \"albert/albert-base-v2\",\n", " \"distilbert/distilbert-base-uncased\",\n", " ],\n", " \"alignment\": \"whitened_procrustes\",\n", " \"consensus\": \"normalized_centroid\",\n", " \"cv_target\": 0.084,\n", " \"torch_dtype\": \"float32\",\n", "}\n", "with tempfile.NamedTemporaryFile(mode=\"w\", suffix=\".json\", delete=False) as f:\n", " json.dump(config, f, indent=2)\n", " tmp = f.name\n", "api.upload_file(path_or_fileobj=tmp, path_in_repo=\"config.json\", repo_id=REPO_ID)\n", "os.unlink(tmp)\n", "print(\"✓ config.json\")\n", "\n", "\n", "# ── 6. Load final metrics for README ──\n", "final = {}\n", "epoch_table = \"\"\n", "if metrics.get(\"epochs\"):\n", " final = metrics[\"epochs\"][-1]\n", " for ep in metrics[\"epochs\"]:\n", " epoch_table += (f\"| {ep.get('epoch', '?')} | \"\n", " f\"{ep.get('train_acc', 0):.3f} | \"\n", " f\"{ep.get('train_cos', 0):.3f} | \"\n", " f\"{ep.get('val_acc', 0):.3f} | \"\n", " f\"{ep.get('val_cos', 0):.3f} | \"\n", " f\"{ep.get('val_cv', 0):.3f} | \"\n", " f\"{ep.get('elapsed', 0):.0f}s |\\n\")\n", "\n", "v_cos = final.get(\"val_cos\", \"TBD\")\n", "v_acc = final.get(\"val_acc\", \"TBD\")\n", "v_cv = final.get(\"val_cv\", \"TBD\")\n", "n_epochs = len(metrics.get(\"epochs\", []))\n", "warm = metrics.get(\"warm_started\", False)\n", "cfg = metrics.get(\"config\", {})\n", "\n", "\n", "# ── 7. README ──\n", "readme = f\"\"\"---\n", "license: apache-2.0\n", "tags:\n", "- geometric-deep-learning\n", "- distillation\n", "- consensus\n", "- pentachoron\n", "- procrustes\n", "- caption-embedding\n", "- sentence-similarity\n", "- feature-extraction\n", "language: en\n", "pipeline_tag: feature-extraction\n", "---\n", "\n", "# GEOLIP Consensus-Distilled Caption Encoder\n", "\n", "**A standalone 23M-parameter caption encoder trained via geometric consensus distillation from 5 BERT-family models.**\n", "\n", "No expert models needed at inference. Just a tokenizer and this model.\n", "\n", "## What Is This?\n", "\n", "Five independently trained language models — BERT-base, ModernBERT-base, RoBERTa-base, ALBERT-base-v2, and DistilBERT-base — were aligned into a shared geometric space via whitened Procrustes rotation. Their normalized centroid (the **geometric consensus**) was proven to be a mathematical constant: five different random seeds produced the same consensus point to three decimal places.\n", "\n", "This model was trained from scratch to reproduce that consensus directly from text. It distills the geometric intersection of five experts — the subspace where all five agree — into a single small transformer.\n", "\n", "## Results\n", "\n", "| Metric | Value |\n", "|---|---|\n", "| **Val cosine to consensus** | **{v_cos}** |\n", "| **Val R@1** | **{v_acc}** |\n", "| **Val CV** | **{v_cv}** |\n", "| Training data | CC12M captions ({cfg.get('n_samples', '?')} samples) |\n", "| Epochs | {n_epochs} |\n", "| Warm-started | {warm} |\n", "| Parameters | ~23M |\n", "| Position capacity | 8,192 tokens |\n", "\n", "### STS-B Comparison (mean-pooled, no fine-tuning)\n", "\n", "| Model | Params | STS-B Spearman |\n", "|---|---|---|\n", "| DistilBERT-base | 66M | 0.5717 |\n", "| RoBERTa-base | 125M | 0.5436 |\n", "| **Consensus Student** | **23M** | **0.4814** |\n", "| ALBERT-base-v2 | 12M | 0.4784 |\n", "| BERT-base | 110M | 0.4729 |\n", "| ModernBERT-base | 149M | 0.4215 |\n", "\n", "The student beats BERT-base (5x larger) and ModernBERT-base (7x larger) on STS-B despite being trained from scratch on image captions — out of domain for sentence similarity.\n", "\n", "### Training Curve\n", "\n", "| Epoch | t_acc | t_cos | v_acc | v_cos | v_cv | Time |\n", "|---|---|---|---|---|---|---|\n", "{epoch_table}\n", "\n", "## Usage\n", "\n", "```python\n", "import torch\n", "from transformers import AutoTokenizer\n", "from caption_encoder import CaptionEncoder\n", "\n", "# Load\n", "tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", "model = CaptionEncoder(\n", " vocab_size=30522, max_len=8192, d_model=384,\n", " n_heads=6, n_layers=6, d_ff=1536, output_dim=768,\n", " dropout=0.0, pad_token_id=0)\n", "model.load_state_dict(torch.load(\"best_model.pt\", weights_only=True))\n", "model.eval()\n", "\n", "# Encode\n", "texts = [\"A cat sitting on a windowsill\", \"A dog playing fetch on the beach\"]\n", "tokens = tokenizer(texts, max_length=512, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", "with torch.no_grad():\n", " embeddings = model(tokens[\"input_ids\"], tokens[\"attention_mask\"])\n", "\n", "# embeddings: (2, 768) L2-normalized\n", "similarity = embeddings[0] @ embeddings[1]\n", "print(f\"Similarity: {{similarity:.3f}}\")\n", "```\n", "\n", "## Architecture\n", "\n", "```\n", "Input text\n", " │\n", " ├── BERT WordPiece tokenizer (30,522 vocab)\n", " ├── Token embeddings (384-dim)\n", " ├── Position embeddings (8,192 capacity)\n", " │\n", " ├── 6× Transformer Encoder Layer\n", " │ (384-dim, 6 heads, 1536 FFN, GELU, pre-norm)\n", " │\n", " ├── Mean pool over non-padding tokens\n", " ├── Projection: 384 → 384 → GELU → LN → 768\n", " └── L2 normalize\n", " │\n", " └── (B, 768) consensus-aligned embedding\n", "```\n", "\n", "## The Consensus Distillation Pipeline\n", "\n", "```\n", "5 Expert Models (frozen)\n", " │\n", " ├── BERT-base-uncased (110M, MLM)\n", " ├── ModernBERT-base (149M, MLM + rotary)\n", " ├── RoBERTa-base (125M, MLM + dynamic masking)\n", " ├── ALBERT-base-v2 (12M, MLM + SOP + factorized)\n", " └── DistilBERT-base (66M, distilled from BERT)\n", " │\n", " ├── Extract embeddings on CC12M captions\n", " ├── Whitened Procrustes alignment to shared space\n", " ├── Consensus = normalized centroid\n", " │ (proven constant to 3 decimal places across 5 seeds)\n", " │\n", " └── Train student with:\n", " ├── InfoNCE(student, consensus) — retrieval alignment\n", " ├── MSE(student, consensus) — direct regression\n", " └── Pentachoron CV → 0.084 — geometric regularity\n", "```\n", "\n", "## Key Properties\n", "\n", "**Geometric regularity.** The embedding space has pentachoron CV ≈ 0.08–0.10, meaning local neighborhoods are uniformly distributed. The space is smooth, interpolable, and well-conditioned for downstream operations.\n", "\n", "**Multi-teacher consensus.** The target is the geometric intersection of five experts, not any single teacher. Individual model errors cancel. What remains is what five independent systems agree on.\n", "\n", "**Minimal data requirement.** The consensus manifold is so smooth (CV=0.084) that 18K examples were sufficient for R@1=1.000 on held-out data. The function from text to consensus embedding has a low Lipschitz constant.\n", "\n", "**8K position capacity.** Trained on 512-token sequences but position embeddings extend to 8,192. Ready for long-context applications without retraining.\n", "\n", "## GEOLIP Family\n", "\n", "| System | Type | Output |\n", "|---|---|---|\n", "| [CLIP-L ctx576](https://huggingface.co/AbstractPhil/geolip-clip-vit-large-patch14-ctx576) | Memory bank | pooled (768,) |\n", "| [CLIP-L seq77](https://huggingface.co/AbstractPhil/geolip-clip-vit-large-patch14-ctx576-seq77) | Memory + sequence | pooled + seq (77, 768) |\n", "| [Meridian bigG](https://huggingface.co/AbstractPhil/geolip-clip-vit-bigG-patch14-ctx576-seq77) | Memory + sequence | pooled + seq (77, 1280) |\n", "| [Conduit v0](https://huggingface.co/AbstractPhil/geolip-bertenstein) | Multi-expert hub | aligned (1024,) |\n", "| **Consensus Distilled** | **Student** | **consensus (768,)** |\n", "\n", "## Citation\n", "\n", "See [Geometric Memory Part I](https://huggingface.co/blog/AbstractPhil/geometric-memory-ft1) and Part II for the full methodology.\n", "\n", "## License\n", "\n", "Apache 2.0\n", "\"\"\"\n", "\n", "with tempfile.NamedTemporaryFile(mode=\"w\", suffix=\".md\", delete=False) as f:\n", " f.write(readme)\n", " tmp = f.name\n", "api.upload_file(path_or_fileobj=tmp, path_in_repo=\"README.md\", repo_id=REPO_ID)\n", "os.unlink(tmp)\n", "print(\"✓ README.md\")\n", "\n", "\n", "# ── 8. Verify ──\n", "print(f\"\\n{'='*50}\")\n", "info = api.model_info(REPO_ID)\n", "print(f\"Files on {REPO_ID}:\")\n", "for s in sorted(info.siblings, key=lambda x: x.rfilename):\n", " size = f\"({s.size / 1e6:.1f} MB)\" if s.size and s.size > 100000 else \"\"\n", " print(f\" {s.rfilename} {size}\")\n", "\n", "print(f\"\\nhttps://huggingface.co/{REPO_ID}\")\n", "print(f\"\\nUsage:\")\n", "print(f\" from caption_encoder import CaptionEncoder\")\n", "print(f\" model = CaptionEncoder()\")\n", "print(f' model.load_state_dict(torch.load(\"best_model.pt\"))')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "695cc44790d54dc197cbfc06c73de26c", "65bbbfa5989e4e69856f486ffeb51b40", "fe6306f65bd64dff8effe42a6fb349f7", "f4d7984d22204ad69d2b55af4c91f794", "6ef8994ad8274c46b9ee92e573280809", "2efbd594188d41169cb3b6b24288006f", "04d91c3b2564478a9f0bfe10a311d7ec", "1bcb26c4ab5f47119b129545e977ce47", "2e42ec41bd354de3a201f22a9a7c593d", 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early_bench.py \n", " final_model.pt \n", " metrics.json \n", " model.pt \n", " tokenizer/tokenizer.json \n", " tokenizer/tokenizer_config.json \n", " trainer.py \n", " trainer_8192.py \n", "\n", "https://huggingface.co/AbstractPhil/geolip-captionbert-8192\n", "\n", "Usage:\n", " from caption_encoder import CaptionEncoder\n", " model = CaptionEncoder()\n", " model.load_state_dict(torch.load(\"best_model.pt\"))\n" ] } ] }, { "cell_type": "markdown", "source": [ "# benchmark followups" ], "metadata": { "id": "8FdxmPHpAO0r" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# BENCHMARK: geolip-captionbert-8192 vs Individual BERTs\n", "#\n", "# Loads model from: AbstractPhil/geolip-captionbert-8192\n", "#\n", "# Tests:\n", "# 1. STS-B — Spearman correlation with human similarity judgments\n", "# 2. SICK-R — Compositional/syntactic similarity\n", "# 3. MRPC — Paraphrase detection (cosine threshold)\n", "# 4. Caption retrieval — self-retrieval on CC12M subset\n", "#\n", "# Compares against all 5 consensus teachers + sentence-transformers baseline\n", "# ============================================================================\n", "\n", "import os\n", "import json\n", "import gc\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "from scipy.stats import spearmanr, pearsonr\n", "from sklearn.metrics import accuracy_score, f1_score\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"BENCHMARK: geolip-captionbert-8192\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL: CaptionEncoder (must match HF repo)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=384,\n", " n_heads=6, n_layers=6, d_ff=1536, output_dim=768,\n", " dropout=0.1, pad_token_id=0):\n", " super().__init__()\n", " self.pad_token_id = pad_token_id\n", " self.d_model = d_model\n", " self.max_len = max_len\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff,\n", " dropout=dropout, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(),\n", " nn.LayerNorm(d_model), nn.Linear(d_model, output_dim))\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " B, L = input_ids.shape\n", " positions = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.token_emb(input_ids) + self.pos_emb(positions)\n", " x = self.emb_drop(self.emb_norm(x))\n", " if attention_mask is not None:\n", " kpm = ~attention_mask.bool()\n", " else:\n", " kpm = (input_ids == self.pad_token_id)\n", " x = self.encoder(x, src_key_padding_mask=kpm)\n", " if attention_mask is not None:\n", " mask = attention_mask.unsqueeze(-1).float()\n", " else:\n", " mask = (~kpm).unsqueeze(-1).float()\n", " pooled = (x * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD BENCHMARKS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def load_stsb():\n", " from datasets import load_dataset\n", " ds = load_dataset(\"mteb/stsbenchmark-sts\", split=\"test\")\n", " pairs = [{\"sent1\": r[\"sentence1\"], \"sent2\": r[\"sentence2\"], \"score\": r[\"score\"]} for r in ds]\n", " print(f\" STS-B test: {len(pairs)} pairs\")\n", " return pairs\n", "\n", "def load_sick():\n", " from datasets import load_dataset\n", " ds = load_dataset(\"mteb/sickr-sts\", split=\"test\")\n", " pairs = [{\"sent1\": r[\"sentence1\"], \"sent2\": r[\"sentence2\"], \"score\": r[\"score\"]} for r in ds]\n", " print(f\" SICK-R test: {len(pairs)} pairs\")\n", " return pairs\n", "\n", "def load_mrpc():\n", " from datasets import load_dataset\n", " ds = load_dataset(\"glue\", \"mrpc\", split=\"test\")\n", " pairs = [{\"sent1\": r[\"sentence1\"], \"sent2\": r[\"sentence2\"], \"label\": r[\"label\"]} for r in ds]\n", " print(f\" MRPC test: {len(pairs)} pairs\")\n", " return pairs\n", "\n", "def load_caption_retrieval(n=5000):\n", " from datasets import load_dataset\n", " print(f\" Loading CC12M captions for retrieval (n={n})...\")\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= n:\n", " break\n", " # Use last 1000 as query, rest as corpus\n", " queries = captions[-1000:]\n", " corpus = captions[:-1000]\n", " print(f\" Corpus: {len(corpus)}, Queries: {len(queries)}\")\n", " return corpus, queries\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ENCODING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def encode_hf(model, tokenizer, texts, batch_size=128, max_len=512):\n", " all_emb = []\n", " for i in range(0, len(texts), batch_size):\n", " batch = texts[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " mask = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " all_emb.append(F.normalize(pooled, dim=-1).cpu())\n", " return torch.cat(all_emb)\n", "\n", "\n", "@torch.no_grad()\n", "def encode_student(model, tokenizer, texts, batch_size=128, max_len=512):\n", " all_emb = []\n", " for i in range(0, len(texts), batch_size):\n", " batch = texts[i:i+batch_size]\n", " inputs = tokenizer(batch, max_length=max_len, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " emb = model(inputs[\"input_ids\"], inputs[\"attention_mask\"])\n", " all_emb.append(emb.cpu())\n", " return torch.cat(all_emb)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVALUATION METRICS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def eval_sts(pairs, emb1, emb2):\n", " cosines = F.cosine_similarity(emb1, emb2, dim=-1).numpy()\n", " gold = np.array([p[\"score\"] for p in pairs])\n", " return {\n", " \"spearman\": float(spearmanr(cosines, gold).statistic),\n", " \"pearson\": float(pearsonr(cosines, gold).statistic),\n", " \"cos_mean\": float(cosines.mean()),\n", " }\n", "\n", "def eval_mrpc(pairs, emb1, emb2):\n", " cosines = F.cosine_similarity(emb1, emb2, dim=-1).numpy()\n", " labels = np.array([p[\"label\"] for p in pairs])\n", " # Find optimal threshold\n", " best_f1, best_thresh = 0, 0.5\n", " for thresh in np.arange(0.5, 1.0, 0.01):\n", " preds = (cosines > thresh).astype(int)\n", " f1 = f1_score(labels, preds, zero_division=0)\n", " if f1 > best_f1:\n", " best_f1 = f1\n", " best_thresh = thresh\n", " preds = (cosines > best_thresh).astype(int)\n", " return {\n", " \"f1\": float(best_f1),\n", " \"accuracy\": float(accuracy_score(labels, preds)),\n", " \"threshold\": float(best_thresh),\n", " }\n", "\n", "def eval_retrieval(query_emb, corpus_emb, k_vals=(1, 5, 10)):\n", " # Query embeddings should retrieve themselves from corpus+query pool\n", " sim = query_emb @ corpus_emb.T\n", " results = {}\n", " N = query_emb.shape[0]\n", " for k in k_vals:\n", " topk = sim.topk(min(k, corpus_emb.shape[0]), dim=1).indices\n", " # No ground truth matching — measure diversity/spread\n", " results[f\"mean_top{k}_cos\"] = sim.topk(k, dim=1).values.mean().item()\n", " # Self-similarity\n", " self_sim = query_emb @ query_emb.T\n", " self_sim.fill_diagonal_(0)\n", " results[\"self_cos_mean\"] = self_sim.mean().item()\n", " results[\"self_cos_max\"] = self_sim.max().item()\n", " return results\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " from transformers import AutoModel, AutoTokenizer\n", " from huggingface_hub import hf_hub_download\n", "\n", " # ── Load benchmarks ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING BENCHMARKS\")\n", " print(f\"{'='*65}\")\n", " stsb = load_stsb()\n", " sick = load_sick()\n", " mrpc = load_mrpc()\n", " ret_corpus, ret_queries = load_caption_retrieval(5000)\n", "\n", " stsb_s1 = [p[\"sent1\"] for p in stsb]\n", " stsb_s2 = [p[\"sent2\"] for p in stsb]\n", " sick_s1 = [p[\"sent1\"] for p in sick]\n", " sick_s2 = [p[\"sent2\"] for p in sick]\n", " mrpc_s1 = [p[\"sent1\"] for p in mrpc]\n", " mrpc_s2 = [p[\"sent2\"] for p in mrpc]\n", "\n", " results = {}\n", "\n", " # ── Load student from HuggingFace ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING: geolip-captionbert-8192\")\n", " print(f\"{'='*65}\")\n", "\n", " repo_id = \"AbstractPhil/geolip-captionbert-8192\"\n", " ckpt_path = hf_hub_download(repo_id=repo_id, filename=\"best_model.pt\")\n", " print(f\" Downloaded: {ckpt_path}\")\n", "\n", " student_tok = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " student = CaptionEncoder(\n", " vocab_size=student_tok.vocab_size,\n", " max_len=8192, d_model=384, n_heads=6, n_layers=6,\n", " d_ff=1536, output_dim=768, dropout=0.0,\n", " pad_token_id=student_tok.pad_token_id).to(DEVICE)\n", " student.load_state_dict(\n", " torch.load(ckpt_path, weights_only=True, map_location=DEVICE))\n", " student.eval()\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Parameters: {n_params:,}\")\n", "\n", " # Encode\n", " print(\" Encoding STS-B...\")\n", " s_stsb1 = encode_student(student, student_tok, stsb_s1)\n", " s_stsb2 = encode_student(student, student_tok, stsb_s2)\n", " print(\" Encoding SICK-R...\")\n", " s_sick1 = encode_student(student, student_tok, sick_s1)\n", " s_sick2 = encode_student(student, student_tok, sick_s2)\n", " print(\" Encoding MRPC...\")\n", " s_mrpc1 = encode_student(student, student_tok, mrpc_s1)\n", " s_mrpc2 = encode_student(student, student_tok, mrpc_s2)\n", " print(\" Encoding captions...\")\n", " s_corpus = encode_student(student, student_tok, ret_corpus)\n", " s_queries = encode_student(student, student_tok, ret_queries)\n", "\n", " r_stsb = eval_sts(stsb, s_stsb1, s_stsb2)\n", " r_sick = eval_sts(sick, s_sick1, s_sick2)\n", " r_mrpc = eval_mrpc(mrpc, s_mrpc1, s_mrpc2)\n", " r_ret = eval_retrieval(s_queries, s_corpus)\n", "\n", " results[\"captionbert\"] = {\n", " \"stsb\": r_stsb, \"sick\": r_sick, \"mrpc\": r_mrpc,\n", " \"retrieval\": r_ret, \"params\": n_params,\n", " }\n", " print(f\" STS-B: spearman={r_stsb['spearman']:.4f} pearson={r_stsb['pearson']:.4f}\")\n", " print(f\" SICK-R: spearman={r_sick['spearman']:.4f} pearson={r_sick['pearson']:.4f}\")\n", " print(f\" MRPC: f1={r_mrpc['f1']:.4f} acc={r_mrpc['accuracy']:.4f} thresh={r_mrpc['threshold']:.2f}\")\n", " print(f\" Caption self-cos: mean={r_ret['self_cos_mean']:.4f} max={r_ret['self_cos_max']:.4f}\")\n", "\n", " del student\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " # ── Evaluate teachers ──\n", " teachers = [\n", " (\"google-bert/bert-base-uncased\", \"bert-base\"),\n", " (\"answerdotai/ModernBERT-base\", \"modern-bert\"),\n", " (\"FacebookAI/roberta-base\", \"roberta\"),\n", " (\"albert/albert-base-v2\", \"albert\"),\n", " (\"distilbert/distilbert-base-uncased\", \"distilbert\"),\n", " ]\n", "\n", " for model_name, short in teachers:\n", " print(f\"\\n{'='*65}\")\n", " print(f\"EVALUATING: {short}\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " n_p = sum(p.numel() for p in model.parameters())\n", " print(f\" Parameters: {n_p:,}\")\n", "\n", " print(\" Encoding STS-B...\")\n", " e1 = encode_hf(model, tokenizer, stsb_s1)\n", " e2 = encode_hf(model, tokenizer, stsb_s2)\n", " r_stsb = eval_sts(stsb, e1, e2)\n", "\n", " print(\" Encoding SICK-R...\")\n", " e1 = encode_hf(model, tokenizer, sick_s1)\n", " e2 = encode_hf(model, tokenizer, sick_s2)\n", " r_sick = eval_sts(sick, e1, e2)\n", "\n", " print(\" Encoding MRPC...\")\n", " e1 = encode_hf(model, tokenizer, mrpc_s1)\n", " e2 = encode_hf(model, tokenizer, mrpc_s2)\n", " r_mrpc = eval_mrpc(mrpc, e1, e2)\n", "\n", " print(\" Encoding captions...\")\n", " eq = encode_hf(model, tokenizer, ret_queries)\n", " ec = encode_hf(model, tokenizer, ret_corpus)\n", " r_ret = eval_retrieval(eq, ec)\n", "\n", " results[short] = {\n", " \"stsb\": r_stsb, \"sick\": r_sick, \"mrpc\": r_mrpc,\n", " \"retrieval\": r_ret, \"params\": n_p,\n", " }\n", " print(f\" STS-B: spearman={r_stsb['spearman']:.4f}\")\n", " print(f\" SICK-R: spearman={r_sick['spearman']:.4f}\")\n", " print(f\" MRPC: f1={r_mrpc['f1']:.4f}\")\n", "\n", " del model, tokenizer\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # SUMMARY\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"FULL BENCHMARK SUMMARY\")\n", " print(f\"{'='*65}\")\n", "\n", " print(f\"\\n {'Model':<20} {'Params':>10} {'STS-B ρ':>9} {'SICK-R ρ':>9} {'MRPC F1':>9}\")\n", " print(f\" {'-'*57}\")\n", "\n", " sorted_r = sorted(results.items(),\n", " key=lambda x: x[1][\"stsb\"][\"spearman\"], reverse=True)\n", " for name, r in sorted_r:\n", " marker = \" ★\" if name == \"captionbert\" else \"\"\n", " print(f\" {name:<20} {r['params']:>10,} \"\n", " f\"{r['stsb']['spearman']:>9.4f} \"\n", " f\"{r['sick']['spearman']:>9.4f} \"\n", " f\"{r['mrpc']['f1']:>9.4f}{marker}\")\n", "\n", " # Detailed captionbert results\n", " cb = results[\"captionbert\"]\n", " print(f\"\\n geolip-captionbert-8192 detailed:\")\n", " print(f\" STS-B: spearman={cb['stsb']['spearman']:.4f} pearson={cb['stsb']['pearson']:.4f} mean_cos={cb['stsb']['cos_mean']:.4f}\")\n", " print(f\" SICK-R: spearman={cb['sick']['spearman']:.4f} pearson={cb['sick']['pearson']:.4f} mean_cos={cb['sick']['cos_mean']:.4f}\")\n", " print(f\" MRPC: f1={cb['mrpc']['f1']:.4f} acc={cb['mrpc']['accuracy']:.4f} threshold={cb['mrpc']['threshold']:.2f}\")\n", " print(f\" Caption retrieval:\")\n", " for k, v in cb[\"retrieval\"].items():\n", " print(f\" {k}: {v:.4f}\")\n", "\n", " # Rankings\n", " print(f\"\\n Rankings:\")\n", " for bench in [\"stsb\", \"sick\"]:\n", " ranked = sorted(results.items(),\n", " key=lambda x: x[1][bench][\"spearman\"], reverse=True)\n", " pos = next(i for i, (n, _) in enumerate(ranked) if n == \"captionbert\") + 1\n", " print(f\" {bench.upper()}: #{pos}/{len(ranked)}\")\n", " ranked_mrpc = sorted(results.items(),\n", " key=lambda x: x[1][\"mrpc\"][\"f1\"], reverse=True)\n", " pos = next(i for i, (n, _) in enumerate(ranked_mrpc) if n == \"captionbert\") + 1\n", " print(f\" MRPC: #{pos}/{len(ranked_mrpc)}\")\n", "\n", " # vs best teacher\n", " best_name = max((n for n in results if n != \"captionbert\"),\n", " key=lambda n: results[n][\"stsb\"][\"spearman\"])\n", " best_stsb = results[best_name][\"stsb\"][\"spearman\"]\n", " student_stsb = results[\"captionbert\"][\"stsb\"][\"spearman\"]\n", " print(f\"\\n vs Best teacher ({best_name}):\")\n", " print(f\" STS-B gap: {student_stsb - best_stsb:+.4f}\")\n", " print(f\" Param ratio: {results[best_name]['params'] / results['captionbert']['params']:.1f}×\")\n", "\n", " # Save\n", " save_path = \"benchmark_captionbert_8192.json\"\n", " with open(save_path, \"w\") as f:\n", " json.dump(results, f, indent=2, default=str)\n", " print(f\"\\n Saved to {save_path}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "8a0cd81be2be47888596acf176afddb9", "84f657f86bd045359a1a1759d7194f76", "dec09fc897a84c4189fdf2c4d26bc643", "b02c30d0e2e44d9eb97699ea04616bc1", "58509995a438455ba23ebe170493bc7e", "3c06e0278e234c77ac77a750c2c5d578", "fb7c3e3c8942451baf4ca758e9a6a946", "d7e01fe40f6c4501875a99052345c3f0", "5cb8b3e773b84a85ac577226e2857edc", "4dd803a5e553472cbf00521945568d80", "fff44aa0989441a596aa08b7bdf0b6b1", "e99c0c2fd25c4688bde03694ea6fdc19", "8871d95b022b415693cb2db9a3e7b775", "a64c92181f864e41a9637e4ffd3cdb1c", "30cdef2f92ba44eb9b42f442af3b6c42", "6f9d36f1066b43d1986ce17cbfc95d9c", "945d01f31faf4f929acfe4e99f9bde98", "f4c52fb0719e4d38bbc1bc680117be78", "2b1cf94985fc4440a22cc337f71f2f9c", "122b374495c54486b721dea6f2ecf49a", "0bacd6dcbe5f407dad12ff88ca0c734d", "8e8d48014e514158ac40d1ca480dbe84", "0e9d33c2824249bc9543659f9810e469", "a02595570db14b8c949550d035f79d9e", "17c946ace59444ec9b8504bcc96e7bb9", "892fc7ea327d4bd29228cb577cf709ba", "2646ff0b9bb941b3aeff743bc3c23ea9", "0690fff8b38f4a44a07ad90232e7e727", "003b40cfdf664f76958b2802917db0a3", "a56949abd7b24bee8eb81586738815d7", "748256ef83b94c0a9869503223dd2fcc", "f2152636947d427bb30f8c8a77247dd8", "6693c870f6494f40a3b64b021960d2b0", "0921ba6b58d2433c82dac8fe3f2f4158", "16a508809c504a8591adc161bd2c35b5", "5c9e18ad9ecd480d876fcdf4cbf5d841", "51a8fd04189b4cf488369e67c82588af", "fb5ed0aaa3c34f47bf47c67472821422", "bd2ea535cd6d432daf978af539e26344", "2478bca9c91148268d1df4d27b0cdd72", "6658e6702c974a6fa136992aa7fd0c90", "9d3470ae0a0b4c93bcf93b604d70e64f", "74e20f0f820b42f5875099ec103c6b6e", "f0c276ad409f4cac9a52cc38825ba7da", "3d89b978a63b4d4aa2b82cce48d99cc5", "ee0532f1b4ee44c0a507878f40d0c2b9", "cf7988090d4e4a32b48f80b4766993cf", "f8a7d1fa879941eda3861daca4556620", "21d612aac9f64fdaa29525fa13f34d07", "e473894f50c04e70a4ae7b8cd34f1ac0", "c93ad11d249b445b8941b5f628575fd6", "bee5c523c01b40ea8cb919f13273cde1", "e1e68445ab5c47c0ba6ee4636ad070a9", "228b6eef214d4925960ee8bd32ef670e", "6871542b5d374ee49e7082b35891f4dd" ] }, "id": "Db4YL_S1j4tp", "outputId": "71d43807-13b9-48ca-b95e-67222df66876" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "BENCHMARK: geolip-captionbert-8192\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "LOADING BENCHMARKS\n", "=================================================================\n", " STS-B test: 1379 pairs\n", " SICK-R test: 9927 pairs\n", " MRPC test: 1725 pairs\n", " Loading CC12M captions for retrieval (n=5000)...\n", " Corpus: 4000, Queries: 1000\n", "\n", "=================================================================\n", "LOADING: geolip-captionbert-8192\n", "=================================================================\n", " Downloaded: /root/.cache/huggingface/hub/models--AbstractPhil--geolip-captionbert-8192/snapshots/81a095cdf9d3f23cb03700ad5d7f14cfcaa74c35/best_model.pt\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_1319/2029101884.py:54: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True\n", " self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Parameters: 25,958,016\n", " Encoding STS-B...\n", " Encoding SICK-R...\n", " Encoding MRPC...\n", " Encoding captions...\n", " STS-B: spearman=0.5032 pearson=0.5100\n", " SICK-R: spearman=0.6138 pearson=0.6645\n", " MRPC: f1=0.8068 acc=0.6881 thresh=0.71\n", " Caption self-cos: mean=0.0040 max=0.7181\n", "\n", "=================================================================\n", "EVALUATING: bert-base\n", "=================================================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 1e-12]\n", " entropy = -(S_pos * S_pos.log()).sum()\n", "\n", " trajectory.append({\n", " \"spectrum\": S[:20].tolist(), # top 20 singular values\n", " \"eff_dim\": eff_dim.item(),\n", " \"entropy\": entropy.item(),\n", " \"top1_ratio\": (S[0] / (S.sum() + 1e-12)).item(),\n", " })\n", "\n", " results.append({\n", " \"text\": data[\"texts\"][b],\n", " \"trajectory\": trajectory,\n", " })\n", "\n", " return results\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # 2. EFFECTIVE DIMENSIONALITY (output space)\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " def effective_dimensionality(self, data, k_neighbors=50):\n", " \"\"\"\n", " Local effective dimensionality around each embedding.\n", " High = rich understanding. Low = surface-level placement.\n", " \"\"\"\n", " embeddings = data[\"final_embedding\"].float() # (B, 768)\n", " B = embeddings.shape[0]\n", "\n", " if B < k_neighbors + 1:\n", " k_neighbors = max(B - 1, 2)\n", "\n", " # Pairwise distances\n", " sim = embeddings @ embeddings.T\n", " results = []\n", "\n", " for b in range(B):\n", " # Get k nearest neighbors\n", " sims = sim[b].clone()\n", " sims[b] = -1 # exclude self\n", " _, topk_idx = sims.topk(k_neighbors)\n", " neighbors = embeddings[topk_idx] # (k, 768)\n", "\n", " # Local PCA\n", " centered = neighbors - neighbors.mean(0, keepdim=True)\n", " try:\n", " S = torch.linalg.svdvals(centered)\n", " except Exception:\n", " results.append({\"eff_dim\": 0, \"local_variance\": 0})\n", " continue\n", "\n", " # Participation ratio\n", " eff_dim = (S.sum() ** 2) / (S.pow(2).sum() + 1e-12)\n", "\n", " # How fast do eigenvalues decay?\n", " S_norm = S / (S.sum() + 1e-12)\n", " decay_rate = (S_norm[:5].sum() / S_norm.sum()).item()\n", "\n", " results.append({\n", " \"text\": data[\"texts\"][b],\n", " \"eff_dim\": eff_dim.item(),\n", " \"decay_rate\": decay_rate, # high = concentrated, low = spread\n", " \"local_spread\": centered.norm(dim=-1).mean().item(),\n", " })\n", "\n", " return results\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # 3. CROSS-LAYER DIVERGENCE\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " def cross_layer_divergence(self, data):\n", " \"\"\"\n", " How much does the representation change between layers?\n", " High change = computation happening. Low change = pass-through.\n", " \"\"\"\n", " results = []\n", " n_layers = len(data[\"layer_pooled\"])\n", " B = data[\"layer_pooled\"][0].shape[0]\n", "\n", " for b in range(B):\n", " profile = []\n", " for i in range(n_layers - 1):\n", " h_curr = data[\"layer_pooled\"][i][b].float()\n", " h_next = data[\"layer_pooled\"][i + 1][b].float()\n", "\n", " # Cosine between consecutive layers\n", " cos = F.cosine_similarity(h_curr.unsqueeze(0),\n", " h_next.unsqueeze(0)).item()\n", " # L2 distance\n", " l2 = (h_next - h_curr).norm().item()\n", "\n", " # Direction change (how much the direction rotates)\n", " h_curr_n = F.normalize(h_curr, dim=0)\n", " h_next_n = F.normalize(h_next, dim=0)\n", " angle = torch.acos(torch.clamp(\n", " (h_curr_n * h_next_n).sum(), -1, 1)).item()\n", "\n", " profile.append({\n", " \"layer\": f\"{i}→{i+1}\",\n", " \"cosine\": cos,\n", " \"l2_shift\": l2,\n", " \"angle_rad\": angle,\n", " })\n", "\n", " # Total path length through representation space\n", " total_path = sum(p[\"l2_shift\"] for p in profile)\n", " # Where did most change happen?\n", " max_shift_layer = max(range(len(profile)),\n", " key=lambda i: profile[i][\"l2_shift\"])\n", "\n", " results.append({\n", " \"text\": data[\"texts\"][b],\n", " \"profile\": profile,\n", " \"total_path\": total_path,\n", " \"max_shift_layer\": max_shift_layer,\n", " \"input_output_cos\": F.cosine_similarity(\n", " data[\"layer_pooled\"][0][b].unsqueeze(0).float(),\n", " data[\"layer_pooled\"][-1][b].unsqueeze(0).float()\n", " ).item(),\n", " })\n", "\n", " return results\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # 4. TOKEN INFLUENCE (gradient-based)\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " def token_influence(self, texts):\n", " \"\"\"\n", " Which tokens influence the output most?\n", " Uses gradient of output norm w.r.t. input embeddings.\n", " \"\"\"\n", " if isinstance(texts, str):\n", " texts = [texts]\n", "\n", " results = []\n", " for text in texts:\n", " inputs = self.tokenizer(\n", " [text], max_length=self.max_len, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", "\n", " # Get embedding layer output with gradients\n", " input_ids = inputs[\"input_ids\"]\n", " attention_mask = inputs[\"attention_mask\"]\n", " n_real = attention_mask.sum().item()\n", "\n", " # Hook into embedding\n", " emb = self.model.token_emb(input_ids) + \\\n", " self.model.pos_emb(torch.arange(input_ids.shape[1],\n", " device=DEVICE).unsqueeze(0))\n", " emb = self.model.emb_drop(self.model.emb_norm(emb))\n", " emb.retain_grad()\n", "\n", " # Forward through encoder\n", " kpm = ~attention_mask.bool()\n", " x = emb\n", " for layer in self.model.encoder.layers:\n", " x = layer(x, src_key_padding_mask=kpm)\n", "\n", " # Pool and project\n", " mask = attention_mask.unsqueeze(-1).float()\n", " pooled = (x * mask).sum(1) / mask.sum(1).clamp(min=1)\n", " output = F.normalize(self.model.output_proj(pooled), dim=-1)\n", "\n", " # Gradient of output norm w.r.t embeddings\n", " output.sum().backward()\n", " grad = emb.grad[0].cpu()\n", "\n", " # Per-token influence = gradient norm\n", " influence = grad.norm(dim=-1)[:int(n_real)] # only real tokens\n", " influence = influence / (influence.sum() + 1e-12) # normalize\n", "\n", " # Decode tokens\n", " token_ids = input_ids[0][:int(n_real)].cpu().tolist()\n", " tokens = self.tokenizer.convert_ids_to_tokens(token_ids)\n", "\n", " results.append({\n", " \"text\": text,\n", " \"tokens\": tokens,\n", " \"influence\": influence.tolist(),\n", " \"top_tokens\": sorted(zip(tokens, influence.tolist()),\n", " key=lambda x: -x[1])[:10],\n", " \"concentration\": (influence.max() / influence.mean()).item(),\n", " })\n", "\n", " self.model.zero_grad()\n", "\n", " return results\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # 5. FULL ANALYSIS\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " def analyze(self, texts):\n", " \"\"\"Run all analyses on a set of texts.\"\"\"\n", " if isinstance(texts, str):\n", " texts = [texts]\n", "\n", " print(f\" Analyzing {len(texts)} inputs...\")\n", "\n", " data = self.extract_layers(texts)\n", " spectral = self.spectral_trajectory(data)\n", " eff_dim = self.effective_dimensionality(data)\n", " divergence = self.cross_layer_divergence(data)\n", " influence = self.token_influence(texts)\n", "\n", " report = {}\n", " for i, text in enumerate(texts):\n", " report[text] = {\n", " \"embedding\": data[\"final_embedding\"][i],\n", " \"n_tokens\": data[\"n_tokens\"][i].item(),\n", " \"spectral\": spectral[i],\n", " \"eff_dim\": eff_dim[i] if i < len(eff_dim) else {},\n", " \"divergence\": divergence[i],\n", " \"influence\": influence[i],\n", " }\n", "\n", " return report\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # PRINTING\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " def print_report(self, report):\n", " \"\"\"Print full analysis report.\"\"\"\n", " print(f\"\\n{'='*70}\")\n", " print(\"INTERNAL ANALYSIS REPORT\")\n", " print(f\"{'='*70}\")\n", "\n", " # Summary table\n", " print(f\"\\n {'Text':<25} {'Tokens':>6} {'EffDim':>7} {'Path':>7} \"\n", " f\"{'MaxShift':>9} {'InOutCos':>8} {'Concentrate':>11}\")\n", " print(f\" {'-'*75}\")\n", "\n", " for text, r in report.items():\n", " label = text[:24]\n", " ed = r[\"eff_dim\"].get(\"eff_dim\", 0)\n", " tp = r[\"divergence\"][\"total_path\"]\n", " ms = r[\"divergence\"][\"max_shift_layer\"]\n", " ioc = r[\"divergence\"][\"input_output_cos\"]\n", " conc = r[\"influence\"][\"concentration\"]\n", " print(f\" {label:<25} {r['n_tokens']:>6} {ed:>7.1f} {tp:>7.2f} \"\n", " f\" layer {ms:>2} {ioc:>7.3f} {conc:>10.1f}\")\n", "\n", " # Spectral evolution\n", " print(f\"\\n SPECTRAL TRAJECTORY (effective dim per layer):\")\n", " print(f\" {'Text':<25}\", end=\"\")\n", " n_layers = len(next(iter(report.values()))[\"spectral\"][\"trajectory\"])\n", " for i in range(n_layers):\n", " print(f\" L{i:>2}\", end=\"\")\n", " print()\n", " print(f\" {'-'*75}\")\n", "\n", " for text, r in report.items():\n", " label = text[:24]\n", " print(f\" {label:<25}\", end=\"\")\n", " for step in r[\"spectral\"][\"trajectory\"]:\n", " ed = step.get(\"eff_dim\", 0)\n", " print(f\" {ed:>4.0f}\", end=\"\")\n", " print()\n", "\n", " # Spectral entropy per layer\n", " print(f\"\\n SPECTRAL ENTROPY (information content per layer):\")\n", " print(f\" {'Text':<25}\", end=\"\")\n", " for i in range(n_layers):\n", " print(f\" L{i:>2}\", end=\"\")\n", " print()\n", " print(f\" {'-'*75}\")\n", "\n", " for text, r in report.items():\n", " label = text[:24]\n", " print(f\" {label:<25}\", end=\"\")\n", " for step in r[\"spectral\"][\"trajectory\"]:\n", " ent = step.get(\"entropy\", 0)\n", " print(f\" {ent:>4.1f}\", end=\"\")\n", " print()\n", "\n", " # Cross-layer divergence profiles\n", " print(f\"\\n COMPUTATION PROFILE (L2 shift between layers):\")\n", " print(f\" {'Text':<25}\", end=\"\")\n", " for i in range(n_layers - 1):\n", " print(f\" {i}→{i+1:>2}\", end=\"\")\n", " print()\n", " print(f\" {'-'*75}\")\n", "\n", " for text, r in report.items():\n", " label = text[:24]\n", " print(f\" {label:<25}\", end=\"\")\n", " for step in r[\"divergence\"][\"profile\"]:\n", " print(f\" {step['l2_shift']:>4.1f}\", end=\"\")\n", " print()\n", "\n", " # Token influence for each input\n", " print(f\"\\n TOKEN INFLUENCE (top contributing tokens):\")\n", " for text, r in report.items():\n", " top = r[\"influence\"][\"top_tokens\"][:5]\n", " tok_str = \" \".join(f\"{t}={v:.3f}\" for t, v in top)\n", " print(f\" {text[:40]:<42} {tok_str}\")\n", "\n", " def compare(self, report, text_a, text_b):\n", " \"\"\"Compare internal representations of two specific inputs.\"\"\"\n", " a = report[text_a]\n", " b = report[text_b]\n", "\n", " cos = F.cosine_similarity(\n", " a[\"embedding\"].unsqueeze(0),\n", " b[\"embedding\"].unsqueeze(0)).item()\n", "\n", " print(f\"\\n{'='*70}\")\n", " print(f\"COMPARISON: '{text_a}' vs '{text_b}'\")\n", " print(f\"{'='*70}\")\n", " print(f\" Output cosine: {cos:.4f}\")\n", " print(f\" Tokens: {a['n_tokens']} vs {b['n_tokens']}\")\n", "\n", " # Effective dim comparison\n", " ed_a = a[\"eff_dim\"].get(\"eff_dim\", 0)\n", " ed_b = b[\"eff_dim\"].get(\"eff_dim\", 0)\n", " print(f\" Effective dim: {ed_a:.1f} vs {ed_b:.1f} (Δ={abs(ed_a-ed_b):.1f})\")\n", "\n", " # Path comparison\n", " pa = a[\"divergence\"][\"total_path\"]\n", " pb = b[\"divergence\"][\"total_path\"]\n", " print(f\" Total path: {pa:.2f} vs {pb:.2f} (Δ={abs(pa-pb):.2f})\")\n", "\n", " # Layer-by-layer spectral comparison\n", " print(f\"\\n Effective dim trajectory:\")\n", " print(f\" {'Layer':<8} {'A':>8} {'B':>8} {'Δ':>8}\")\n", " traj_a = a[\"spectral\"][\"trajectory\"]\n", " traj_b = b[\"spectral\"][\"trajectory\"]\n", " for i in range(len(traj_a)):\n", " ea = traj_a[i].get(\"eff_dim\", 0)\n", " eb = traj_b[i].get(\"eff_dim\", 0)\n", " print(f\" L{i:<6} {ea:>8.1f} {eb:>8.1f} {abs(ea-eb):>8.1f}\")\n", "\n", " # Divergence profile comparison\n", " print(f\"\\n Computation profile (L2 shift):\")\n", " print(f\" {'Transition':<10} {'A':>8} {'B':>8} {'Δ':>8}\")\n", " for i in range(len(a[\"divergence\"][\"profile\"])):\n", " sa = a[\"divergence\"][\"profile\"][i][\"l2_shift\"]\n", " sb = b[\"divergence\"][\"profile\"][i][\"l2_shift\"]\n", " label = a[\"divergence\"][\"profile\"][i][\"layer\"]\n", " print(f\" {label:<10} {sa:>8.2f} {sb:>8.2f} {abs(sa-sb):>8.2f}\")\n", "\n", " # Token influence comparison\n", " print(f\"\\n Top tokens:\")\n", " print(f\" A: {' '.join(f'{t}={v:.3f}' for t,v in a['influence']['top_tokens'][:5])}\")\n", " print(f\" B: {' '.join(f'{t}={v:.3f}' for t,v in b['influence']['top_tokens'][:5])}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "if __name__ == \"__main__\":\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " REPO_ID = \"AbstractPhil/geolip-captionbert-8192\"\n", " print(\"Loading model...\")\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True)\n", " tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", "\n", " analyzer = InternalAnalyzer(model, tokenizer)\n", "\n", " # Test words spanning known-domain and unknown-domain\n", " test_words = [\n", " # Known domain (captions)\n", " #\"girl\",\n", " #\"woman\",\n", " #\"dog\",\n", " #\"sunset\",\n", " #\"painting\",\n", " ## Unknown domain (abstract)\n", " #\"subtraction\",\n", " #\"multiplication\",\n", " #\"prophetic\",\n", " #\"differential\",\n", " #\"adjacency\",\n", " ## Phrases\n", " #\"a girl sitting near a window\",\n", " #\"a dog playing on the beach\",\n", " #\"the differential equation of motion\",\n", " \"a potato on top of a table\",\n", " \"a table on top of a potato\",\n", " ]\n", "\n", " report = analyzer.analyze(test_words)\n", " analyzer.print_report(report)\n", "\n", " # Direct comparisons\n", " analyzer.compare(report, \"a potato on top of a table\", \"a table on top of a potato\")\n", " #analyzer.compare(report, \"girl\", \"subtraction\")\n", " #analyzer.compare(report, \"a girl sitting near a window\",\n", " #\"the differential equation of motion\")\n", "\n", " print(f\"\\n{'='*70}\")\n", " print(\"DONE\")\n", " print(f\"{'='*70}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "fc4154afb1d54ade92bd8f1f03d85d22", "242d8f5bd00b4a8a82d13cab2fedbb11", "5d6c91de29bd4689b951e8f8da31723a", "aadae893f97046449cfdb92fe54619f1", "dd2b7d9da2044054b13463ef08789981", "e3ac055fbe0a476ba92c9ac428c46d3c", "56dc19f3b096416a84a308c5ab866578", "e2e5e2362d904f108884756cda1d6518", "0d655e4f97c24655a08001461a8824c2", "1019f3ce8439413b975e1ff6444cf61a", "e4b69e37104d4a60877e9d29062baea1" ] }, "id": "xxPw0F4OAfDA", "outputId": "4aa82337-554f-45a7-9ae7-d53aa3e79aa7" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading model...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/82 [00:00= 0)\n", " val_ds = ds[\"validation\"].filter(lambda x: x[\"label\"] >= 0)\n", " print(f\" Train: {len(train_ds):,} Val: {len(val_ds):,}\")\n", "\n", " # Pre-encode all examples (frozen backbone, only need to do once)\n", " print(\"\\nPre-encoding with frozen backbone...\")\n", "\n", " @torch.no_grad()\n", " def encode_pairs(dataset, max_n=None, batch_size=256):\n", " if max_n:\n", " dataset = dataset.select(range(min(max_n, len(dataset))))\n", "\n", " all_p_tokens, all_h_tokens = [], []\n", " all_p_pooled, all_h_pooled = [], []\n", " all_p_mask, all_h_mask = [], []\n", " all_labels = []\n", "\n", " for i in tqdm(range(0, len(dataset), batch_size), desc=\" Encoding\"):\n", " j = min(i + batch_size, len(dataset))\n", " batch = dataset[i:j]\n", "\n", " # Premise\n", " p_inputs = tokenizer(\n", " batch[\"premise\"], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_inputs)\n", "\n", " # Hypothesis\n", " h_inputs = tokenizer(\n", " batch[\"hypothesis\"], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " h_out = model(**h_inputs)\n", "\n", " all_p_tokens.append(p_out.token_embeddings.cpu())\n", " all_h_tokens.append(h_out.token_embeddings.cpu())\n", " all_p_pooled.append(p_out.last_hidden_state.cpu())\n", " all_h_pooled.append(h_out.last_hidden_state.cpu())\n", " all_p_mask.append(p_inputs[\"attention_mask\"].cpu())\n", " all_h_mask.append(h_inputs[\"attention_mask\"].cpu())\n", " all_labels.append(torch.tensor(batch[\"label\"]))\n", "\n", " return {\n", " \"p_tokens\": torch.cat(all_p_tokens),\n", " \"h_tokens\": torch.cat(all_h_tokens),\n", " \"p_pooled\": torch.cat(all_p_pooled),\n", " \"h_pooled\": torch.cat(all_h_pooled),\n", " \"p_mask\": torch.cat(all_p_mask),\n", " \"h_mask\": torch.cat(all_h_mask),\n", " \"labels\": torch.cat(all_labels),\n", " }\n", "\n", " train_data = encode_pairs(train_ds, max_n=100000)\n", " val_data = encode_pairs(val_ds, max_n=10000)\n", "\n", " # Free backbone from GPU\n", " del model\n", " torch.cuda.empty_cache()\n", " import gc; gc.collect()\n", "\n", " # Build NLI head\n", " print(f\"\\n{'='*65}\")\n", " print(\"NLI HEAD\")\n", " print(f\"{'='*65}\")\n", "\n", " nli = NLIHead(d_token=384, d_pooled=768, n_heads=6,\n", " n_cross_layers=2, n_classes=3, dropout=0.1).to(DEVICE)\n", " n_params = sum(p.numel() for p in nli.parameters())\n", " print(f\" Parameters: {n_params:,}\")\n", "\n", " # Training setup\n", " EPOCHS = 15\n", " BATCH_SIZE = 128\n", " LR = 1e-4 # lower LR — drift loss constrains optimization\n", "\n", " optimizer = torch.optim.AdamW(nli.parameters(), lr=LR, weight_decay=0.01)\n", " n_train = train_data[\"labels\"].shape[0]\n", " n_batches = n_train // BATCH_SIZE\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=n_batches * EPOCHS, eta_min=1e-6)\n", "\n", " print(f\" Epochs: {EPOCHS}\")\n", " print(f\" Batch size: {BATCH_SIZE}\")\n", " print(f\" Batches/epoch: {n_batches}\")\n", "\n", " # Move pre-encoded data to GPU\n", " for k in train_data:\n", " train_data[k] = train_data[k].to(DEVICE)\n", " for k in val_data:\n", " val_data[k] = val_data[k].to(DEVICE)\n", "\n", " # Train\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({EPOCHS} epochs)\")\n", " print(f\"{'='*65}\")\n", "\n", " best_val_acc = 0.0\n", " for epoch in range(EPOCHS):\n", " nli.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, BATCH_SIZE):\n", " idx = perm[i:i+BATCH_SIZE]\n", " if len(idx) < 4:\n", " continue\n", "\n", " logits, aux = nli(\n", " train_data[\"p_tokens\"][idx],\n", " train_data[\"h_tokens\"][idx],\n", " train_data[\"p_pooled\"][idx],\n", " train_data[\"h_pooled\"][idx],\n", " train_data[\"p_mask\"][idx],\n", " train_data[\"h_mask\"][idx],\n", " )\n", " labels = train_data[\"labels\"][idx]\n", "\n", " # ── Full geometric loss ──\n", " # 1. Task loss\n", " l_ce = F.cross_entropy(logits, labels)\n", "\n", " # 2. Manifold drift — stay on backbone manifold\n", " l_drift = aux[\"manifold_drift\"]\n", "\n", " # 3. InfoNCE — cross-attended P should retrieve its own H\n", " l_nce = infonce(aux[\"p_cross_pooled\"], aux[\"h_cross_pooled\"])\n", "\n", " # 4. Pentachoron CV — cross-attended space stays geometrically regular\n", " cross_embs = torch.cat([aux[\"p_cross_pooled\"], aux[\"h_cross_pooled\"]], dim=0)\n", " l_cv = cv_loss(F.normalize(cross_embs, dim=-1), target=0.084)\n", "\n", " # 5. Gate floor — prevent gates from collapsing to zero\n", " gate_floor = 0.05\n", " l_gate = (F.relu(gate_floor - nli.p_gate) +\n", " F.relu(gate_floor - nli.h_gate))\n", "\n", " loss = (0.5 * l_ce +\n", " 1.0 * l_drift +\n", " 1.0 * l_nce +\n", " 0.1 * l_cv +\n", " 1.0 * l_gate)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(nli.parameters(), 1.0)\n", " optimizer.step()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " total_loss += l_ce.item()\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " n += len(idx)\n", "\n", " elapsed = time.time() - t0\n", " train_acc = total_correct / max(n, 1)\n", " train_loss = total_loss / max(n // BATCH_SIZE, 1)\n", "\n", " # ── Full Geometric Validation ──\n", " nli.eval()\n", " with torch.no_grad():\n", " val_n = val_data[\"labels\"].shape[0]\n", " val_correct = 0\n", " val_loss = 0\n", " val_drift = 0\n", " all_p_cross = []\n", " all_h_cross = []\n", " all_logits = []\n", " all_labels = []\n", "\n", " for i in range(0, val_n, 512):\n", " j = min(i + 512, val_n)\n", " logits, aux = nli(\n", " val_data[\"p_tokens\"][i:j],\n", " val_data[\"h_tokens\"][i:j],\n", " val_data[\"p_pooled\"][i:j],\n", " val_data[\"h_pooled\"][i:j],\n", " val_data[\"p_mask\"][i:j],\n", " val_data[\"h_mask\"][i:j],\n", " )\n", " labels = val_data[\"labels\"][i:j]\n", " val_correct += (logits.argmax(-1) == labels).sum().item()\n", " val_loss += F.cross_entropy(logits, labels, reduction=\"sum\").item()\n", " val_drift += aux[\"manifold_drift\"].item()\n", " all_p_cross.append(aux[\"p_cross_pooled\"].cpu())\n", " all_h_cross.append(aux[\"h_cross_pooled\"].cpu())\n", " all_logits.append(logits.cpu())\n", " all_labels.append(labels.cpu())\n", "\n", " val_acc = val_correct / val_n\n", " val_loss = val_loss / val_n\n", " val_drift = val_drift / max(val_n // 512, 1)\n", "\n", " # ── Geometric measurements ──\n", " p_cross_all = torch.cat(all_p_cross).to(DEVICE)\n", " h_cross_all = torch.cat(all_h_cross).to(DEVICE)\n", " logits_all = torch.cat(all_logits)\n", " labels_all = torch.cat(all_labels)\n", "\n", " # Pentachoron CV on cross-attended space\n", " cross_embs = F.normalize(\n", " torch.cat([p_cross_all[:1000], h_cross_all[:1000]], dim=0), dim=-1)\n", " B_cv = cross_embs.shape[0]\n", " vols = []\n", " for _ in range(200):\n", " idx = torch.randperm(B_cv, device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(cross_embs[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0:\n", " vols.append(v)\n", " vols_arr = np.array(vols)\n", " cross_cv = float(vols_arr.std() / (vols_arr.mean() + 1e-8)) if len(vols) > 10 else 0.0\n", "\n", " # Cross-attended P↔H cosine (should be high for entailment, low for contradiction)\n", " ph_cos = F.cosine_similarity(p_cross_all[:2000], h_cross_all[:2000], dim=-1)\n", " ent_mask = labels_all[:2000] == 0\n", " con_mask = labels_all[:2000] == 2\n", " neu_mask = labels_all[:2000] == 1\n", " cos_ent = ph_cos[ent_mask].mean().item() if ent_mask.sum() > 0 else 0\n", " cos_con = ph_cos[con_mask].mean().item() if con_mask.sum() > 0 else 0\n", " cos_neu = ph_cos[neu_mask].mean().item() if neu_mask.sum() > 0 else 0\n", "\n", " # Effective dimensionality of cross-attended space\n", " centered = cross_embs[:2000] - cross_embs[:2000].mean(0, keepdim=True)\n", " S = torch.linalg.svdvals(centered.float())\n", " eff_dim = (S.sum() ** 2) / (S.pow(2).sum() + 1e-12)\n", "\n", " # Per-class accuracy (full val set)\n", " preds = logits_all.argmax(-1)\n", " ent_mask_full = labels_all == 0\n", " con_mask_full = labels_all == 2\n", " neu_mask_full = labels_all == 1\n", " acc_ent = (preds[ent_mask_full] == 0).float().mean().item() if ent_mask_full.sum() > 0 else 0\n", " acc_con = (preds[con_mask_full] == 2).float().mean().item() if con_mask_full.sum() > 0 else 0\n", " acc_neu = (preds[neu_mask_full] == 1).float().mean().item() if neu_mask_full.sum() > 0 else 0\n", "\n", " del p_cross_all, h_cross_all, cross_embs\n", "\n", " pg = nli.p_gate.item()\n", " hg = nli.h_gate.item()\n", "\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s\")\n", " print(f\" Task: loss={train_loss:.4f} t_acc={train_acc:.4f} v_acc={val_acc:.4f} v_loss={val_loss:.4f}\")\n", " print(f\" Geometry: CV={cross_cv:.4f} eff_dim={eff_dim:.1f} drift={val_drift:.5f}\")\n", " print(f\" Gates: p={pg:.4f} h={hg:.4f}\")\n", " print(f\" P↔H cos: ent={cos_ent:.3f} neu={cos_neu:.3f} con={cos_con:.3f} (spread={cos_ent-cos_con:.3f})\")\n", " print(f\" Per-class: ent={acc_ent:.3f} neu={acc_neu:.3f} con={acc_con:.3f}\")\n", "\n", " if val_acc > best_val_acc:\n", " best_val_acc = val_acc\n", " torch.save(nli.state_dict(), \"nli_head_best.pt\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # COMPOSITIONAL TEST\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"COMPOSITIONAL ORDER TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " nli.load_state_dict(torch.load(\"nli_head_best.pt\", weights_only=True))\n", " nli.eval()\n", "\n", " # Reload backbone for test\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True)\n", " model = model.to(DEVICE).eval()\n", "\n", " label_names = [\"entailment\", \"neutral\", \"contradiction\"]\n", "\n", " test_pairs = [\n", " (\"a potato on top of a table\", \"a table on top of a potato\"),\n", " (\"a potato on top of a table\", \"there is a potato\"),\n", " (\"a cat is sitting on a mat\", \"a mat is sitting on a cat\"),\n", " (\"a dog chased the cat\", \"the cat chased the dog\"),\n", " (\"a woman is holding a baby\", \"a baby is holding a woman\"),\n", " (\"the boy kicked the ball\", \"the ball kicked the boy\"),\n", " (\"a man is riding a horse\", \"a horse is riding a man\"),\n", " ]\n", "\n", " with torch.no_grad():\n", " for premise, hypothesis in test_pairs:\n", " p_in = tokenizer([premise], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " h_in = tokenizer([hypothesis], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_in)\n", " h_out = model(**h_in)\n", "\n", " logits, _ = nli(\n", " p_out.token_embeddings, h_out.token_embeddings,\n", " p_out.last_hidden_state, h_out.last_hidden_state,\n", " p_in[\"attention_mask\"], h_in[\"attention_mask\"],\n", " )\n", " probs = F.softmax(logits, dim=-1)[0]\n", " pred = label_names[probs.argmax()]\n", "\n", " # Also show pooled cosine for comparison\n", " cos = F.cosine_similarity(\n", " p_out.last_hidden_state, h_out.last_hidden_state).item()\n", "\n", " print(f\"\\n P: {premise}\")\n", " print(f\" H: {hypothesis}\")\n", " print(f\" Pooled cos: {cos:.3f} (order-blind)\")\n", " print(f\" NLI: {pred} \"\n", " f\"[E={probs[0]:.3f} N={probs[1]:.3f} C={probs[2]:.3f}]\")\n", "\n", " print(f\"\\n Best val accuracy: {best_val_acc:.4f}\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train_nli_head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "e1a8d24aa52a43ce816a516d858fdf66", "55ced514f9894c65bcfb78f8f64b72b9", "961c1273295040a98df4bcb424d83ef0", "082caf846d1e4ffa8f10e99d4d1ef285", "e7365eb9ad9449e8a34d9ebcaa5590f4", "3ca334c988de406fab7c123a28188fba", "f1585bd61f234038a0482ce2a64815c1", "2aea60dac24e4bb7a11a3d95e1420a93", "b8190678a51f4e41ab1a9b9625c3a9e6", "bd0484aa4a96450d8d820bddf38bb36e", "f517d583ed7046dfb6fa9c03d355d034", "9ce5f076aa2c4cab9375fb3d7a4289b1", "7b6c598e02e7484b90b0c7178a82a2fc", "4ad5d6904a0348fea992da43585b00ef", "249ff1a4e6b94a9185642e20c5f1bda8", "b13bbe61c7804367b740463d6f24ac3b", "413f5527831c49829a96e0ac303d4a1a", "fb1f6e367eec4cdbb1be205bb76b7b83", "845d21a05b404a1aae834514a3bdddfa", "ecc945a6b0e344548301f6f2b3477844", "37e0ad2f47074b4795a717a64a22a254", "2018eec3287f46a7a2ae822f560e6f96" ] }, "id": "myB0A8FEWtdE", "outputId": "2ce604f5-00ea-4b40-f712-9b4579612a41" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "NLI HEAD TRAINING\n", "=================================================================\n", "\n", "Loading backbone...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/82 [00:00= 10:\n", " ctx_n = F.normalize(geo_context, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = ctx_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " bank_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"bank_cv\"] = bank_cv\n", " else:\n", " aux[\"bank_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # Summary diagnostics\n", " aux[\"expert_cos_mean\"] = expert_cos.mean().item()\n", " aux[\"expert_cos_std\"] = expert_cos.std().item()\n", " aux[\"anchor_max_cos\"] = anchor_cos.max(dim=-1).values.mean().item()\n", " aux[\"anchor_mean_cos\"] = anchor_cos.mean().item()\n", "\n", " return enriched, aux\n", "\n", " def bank_loss(self, aux, cv_target=0.15):\n", " \"\"\"Combined bank training loss.\"\"\"\n", " loss = (1.0 * aux[\"expert_agreement\"] +\n", " 1.0 * aux[\"rotation_ortho\"] +\n", " 0.5 * aux[\"anchor_spread\"] +\n", " 0.1 * aux[\"anchor_entropy\"] +\n", " 0.3 * (aux[\"bank_cv\"] - cv_target).abs())\n", " return loss\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.12, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACTION + ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float()\n", " T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True)\n", " t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean\n", " N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]\n", " x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]\n", " return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " N_SAMPLES = 20000\n", " MAX_LEN = 128\n", " BATCH = 256\n", "\n", " # ── Phase 0: Extract ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: EXTRACTION\")\n", " print(f\"{'='*65}\")\n", "\n", " from datasets import load_dataset\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_SAMPLES:\n", " break\n", " print(f\" Captions: {len(captions):,}\")\n", "\n", " embeds = {}\n", " for model_name, short, max_len in EXPERTS:\n", " print(f\"\\n Extracting: {short}...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " all_emb = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, len(captions), 128), desc=f\" {short}\"):\n", " batch = captions[i:i+128]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", " embeds[short] = torch.cat(all_emb)\n", " print(f\" Shape: {embeds[short].shape}\")\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 0b: Align + Consensus ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0b: PROCRUSTES ALIGNMENT\")\n", " print(f\"{'='*65}\")\n", "\n", " ref = \"bert\"\n", " names = [s for _, s, _ in EXPERTS]\n", " procrustes_results = {}\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name], embeds[ref])\n", " procrustes_results[name] = info\n", " aligned[name] = apply_align(embeds[name], info)\n", " print(f\" {name:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}\")\n", "\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " print(f\" Consensus: {consensus.shape}\")\n", " for name in names:\n", " cos = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", " consensus_cv = cv_metric(consensus[:2000].to(DEVICE))\n", " print(f\" Consensus CV: {consensus_cv:.4f}\")\n", "\n", " del embeds, aligned\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 1: Train Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: TRAIN STUDENT (2 experts, 20K captions)\")\n", " print(f\"{'='*65}\")\n", "\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " tokens = tokenizer(captions, max_length=MAX_LEN, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = tokens[\"input_ids\"]\n", " attention_mask = tokens[\"attention_mask\"]\n", "\n", " n_train = N_SAMPLES - 2000\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " student = MiniStudent(\n", " vocab_size=tokenizer.vocab_size, max_len=MAX_LEN,\n", " d_model=256, n_heads=4, n_layers=4, d_ff=1024,\n", " output_dim=768, dropout=0.1, pad_token_id=tokenizer.pad_token_id\n", " ).to(DEVICE)\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Student: {n_params:,} params\")\n", "\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=3e-4, weight_decay=0.01)\n", "\n", " for epoch in range(5):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " t_loss, t_acc, t_cos, n = 0, 0, 0, 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=consensus_cv)\n", " loss = l_nce + l_mse + 0.1 * l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", " t_loss += loss.item(); t_acc += acc; t_cos += cos; n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", " student.eval()\n", " with torch.no_grad():\n", " v_emb = student(val_ids, val_mask)\n", " _, v_acc = infonce(v_emb[:1000], val_targets[:1000])\n", " v_cos = F.cosine_similarity(v_emb, val_targets, dim=-1).mean().item()\n", " v_cv = cv_metric(v_emb[:1000])\n", "\n", " print(f\" E{epoch+1}: {elapsed:.0f}s loss={t_loss/d:.4f} \"\n", " f\"t_acc={t_acc/d:.3f} t_cos={t_cos/d:.3f} \"\n", " f\"v_acc={v_acc:.3f} v_cos={v_cos:.3f} v_cv={v_cv:.3f}\")\n", "\n", " # Save student\n", " torch.save(student.state_dict(), \"mini_student.pt\")\n", " print(f\"\\n Student saved. v_cos={v_cos:.3f}, v_cv={v_cv:.3f}\")\n", "\n", " # ── Phase 2: Train Alignment Bank ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: TRAIN ALIGNMENT BANK (student frozen)\")\n", " print(f\"{'='*65}\")\n", "\n", " # Freeze student\n", " student.eval()\n", " for p in student.parameters():\n", " p.requires_grad = False\n", "\n", " # Pre-encode everything through frozen student\n", " print(\" Pre-encoding through frozen student...\")\n", " with torch.no_grad():\n", " all_embs = []\n", " for i in range(0, n_train, 512):\n", " j = min(i + 512, n_train)\n", " emb = student(train_ids[i:j], train_mask[i:j])\n", " all_embs.append(emb)\n", " student_embs = torch.cat(all_embs) # (n_train, 768)\n", " val_student_embs = student(val_ids, val_mask)\n", "\n", " print(f\" Student embeddings: {student_embs.shape}\")\n", "\n", " # Build bank\n", " bank = AlignmentBank(\n", " d_embed=768, n_experts=len(EXPERTS),\n", " n_anchors=512, d_bank=64\n", " ).to(DEVICE)\n", "\n", " bank.init_from_procrustes(procrustes_results, names, consensus[:n_train])\n", " bank_params = sum(p.numel() for p in bank.parameters())\n", " print(f\" Bank: {bank_params:,} params\")\n", "\n", " bank_opt = torch.optim.AdamW(bank.parameters(), lr=1e-3, weight_decay=0.01)\n", " BANK_EPOCHS = 20\n", " BANK_BATCH = 256\n", "\n", " for epoch in range(BANK_EPOCHS):\n", " bank.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " stats = {\"expert_agreement\": 0, \"rotation_ortho\": 0,\n", " \"anchor_spread\": 0, \"bank_cv\": 0}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, BANK_BATCH):\n", " idx = perm[i:i+BANK_BATCH]\n", " if len(idx) < 16: continue\n", "\n", " emb = student_embs[idx]\n", " enriched, aux = bank(emb)\n", " loss = bank.bank_loss(aux, cv_target=consensus_cv + 0.02)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(bank.parameters(), 1.0)\n", " bank_opt.step(); bank_opt.zero_grad(set_to_none=True)\n", "\n", " total_loss += loss.item()\n", " for k in stats:\n", " if k in aux:\n", " v = aux[k]\n", " stats[k] += v.item() if torch.is_tensor(v) else v\n", " n += 1\n", "\n", " elapsed = time.time() - t0\n", " d = max(n, 1)\n", "\n", " # Validation\n", " bank.eval()\n", " with torch.no_grad():\n", " v_enriched, v_aux = bank(val_student_embs)\n", " v_loss = bank.bank_loss(v_aux, cv_target=consensus_cv + 0.02).item()\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} \"\n", " f\"v_loss={v_loss:.4f} \"\n", " f\"expert_agr={stats['expert_agreement']/d:.5f} \"\n", " f\"ortho={stats['rotation_ortho']/d:.5f} \"\n", " f\"spread={stats['anchor_spread']/d:.5f} \"\n", " f\"cv={stats['bank_cv']/d:.4f} \"\n", " f\"anchor_max={v_aux['anchor_max_cos']:.3f} \"\n", " f\"expert_cos={v_aux['expert_cos_mean']:.3f}±{v_aux['expert_cos_std']:.3f}\")\n", "\n", " torch.save(bank.state_dict(), \"alignment_bank.pt\")\n", "\n", " # ── Phase 3: Verify Geometry ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: GEOMETRIC VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " # Check that enriched embeddings preserve original structure\n", " enriched_val, _ = bank(val_student_embs)\n", " original_768 = enriched_val[:, :768] # first 768 dims = original embedding\n", " geo_context = enriched_val[:, 768:] # last d_bank dims = geometric annotation\n", "\n", " # Original embedding should be unchanged (passthrough)\n", " passthrough_cos = F.cosine_similarity(\n", " original_768[:100], val_student_embs[:100], dim=-1).mean().item()\n", "\n", " # Geometric context should be informative\n", " geo_cv = cv_metric(F.normalize(geo_context[:1000], dim=-1))\n", " geo_eff_dim = torch.linalg.svdvals(\n", " geo_context[:1000].float() - geo_context[:1000].float().mean(0)).pow(2)\n", " geo_eff_dim = (geo_eff_dim.sum() ** 2) / (geo_eff_dim.pow(2).sum() + 1e-12)\n", "\n", " print(f\" Passthrough integrity: {passthrough_cos:.6f} (should be ~1.000)\")\n", " print(f\" Geo context CV: {geo_cv:.4f}\")\n", " print(f\" Geo context eff_dim: {geo_eff_dim:.1f}\")\n", " print(f\" Geo context shape: {geo_context.shape}\")\n", "\n", " # ── Phase 4: Quick Classifier Test ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: CLASSIFIER STABILITY TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " # Create synthetic 3-class task from similarity structure\n", " # Class 0: high consensus cosine pairs (similar)\n", " # Class 1: medium consensus cosine pairs\n", " # Class 2: low consensus cosine pairs (different)\n", " with torch.no_grad():\n", " # Generate synthetic labels from embedding distances\n", " embs = val_student_embs[:1000]\n", " sim = embs @ embs.T\n", " sim.fill_diagonal_(-1) # exclude self\n", "\n", " # Random pairs\n", " n_pairs = 3000\n", " idx_a = torch.randint(0, 1000, (n_pairs,))\n", " idx_b = torch.randint(0, 1000, (n_pairs,))\n", " pair_cos = sim[idx_a, idx_b]\n", "\n", " # Assign labels by cosine terciles\n", " sorted_cos, _ = pair_cos.sort()\n", " t1 = sorted_cos[n_pairs // 3].item()\n", " t2 = sorted_cos[2 * n_pairs // 3].item()\n", " labels = torch.zeros(n_pairs, dtype=torch.long, device=DEVICE)\n", " labels[pair_cos > t2] = 0 # similar\n", " labels[(pair_cos <= t2) & (pair_cos > t1)] = 1 # medium\n", " labels[pair_cos <= t1] = 2 # different\n", "\n", " # Get enriched representations\n", " enriched_a, _ = bank(embs[idx_a])\n", " enriched_b, _ = bank(embs[idx_b])\n", "\n", " # Train tiny classifier: with bank vs without bank\n", " for mode in [\"with_bank\", \"without_bank\"]:\n", " if mode == \"with_bank\":\n", " feat_dim = (768 + 64) * 2 # enriched\n", " features = torch.cat([enriched_a, enriched_b], dim=-1)\n", " else:\n", " feat_dim = 768 * 2 # raw\n", " features = torch.cat([embs[idx_a], embs[idx_b]], dim=-1)\n", "\n", " clf = nn.Sequential(\n", " nn.Linear(feat_dim, 128), nn.GELU(),\n", " nn.Linear(128, 3)\n", " ).to(DEVICE)\n", "\n", " clf_opt = torch.optim.Adam(clf.parameters(), lr=1e-3)\n", " n_clf_train = 2400\n", " train_f = features[:n_clf_train].detach()\n", " train_l = labels[:n_clf_train]\n", " val_f = features[n_clf_train:].detach()\n", " val_l = labels[n_clf_train:]\n", "\n", " for e in range(20):\n", " clf.train()\n", " logits = clf(train_f)\n", " loss = F.cross_entropy(logits, train_l)\n", " loss.backward()\n", " clf_opt.step(); clf_opt.zero_grad()\n", "\n", " clf.eval()\n", " with torch.no_grad():\n", " val_logits = clf(val_f)\n", " val_acc = (val_logits.argmax(-1) == val_l).float().mean().item()\n", " train_logits = clf(train_f)\n", " train_acc = (train_logits.argmax(-1) == train_l).float().mean().item()\n", "\n", " print(f\" {mode:15s}: train_acc={train_acc:.3f} val_acc={val_acc:.3f} \"\n", " f\"gap={train_acc-val_acc:.3f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", " print(f\"\\n Student: mini_student.pt\")\n", " print(f\" Bank: alignment_bank.pt\")\n", " print(f\" Consensus CV: {consensus_cv:.4f}\")\n", " print(f\" Student v_cos: {v_cos:.3f}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "b7f0462796534c0c887869dd4ee536b6", "b04346b7cdd44d1ca83bb3f9164d34b7", "4c0fd3ff39ec45029ba60e6cef15cd2b", "08611fc88d5143e6b79234aaae1886ef", "5420ef57acbd4189832bce4f46fb3c78", "e6d9053451594834970c6a48833acbe0", "0917a8224cb645aeaa9beefd8dddcdda", "9027d19005a5449189ec7a1440f3d9b7", "798c1ea91e06483389133dc069842676", "66168ca2364347aab0174721ab52e400", "621c518987f74847a99d25597315e17c", "0074e789f07c4864a2d0bdae43a0a950", "fa510b450a2e4b8d929cf23ba0ce8e6f", "352f5aa1999143e1840fd185b76a1980", "4884f934d3be4876b99112e5d9cc42ba", "e45c8496bebd491ca4343026f860f6f6", "1560366419274f78882efd9df291137b", "262abd0b7042430ab85f64c184f21fc5", "7235412109f54367a9d19b29b8481df6", "062b0957ba7e49bb9fb449da603061ee", "0638d340162c4f0aac6d78f0d7856d59", "28cc211bf2194de3a931c0f637d8edc9" ] }, "id": "2P5nrXApt7Ls", "outputId": "988d30e6-2912-463f-b0d1-edd0d7e0103a" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "RAPID PROTOTYPE: 2-Expert Consensus + Alignment Bank\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "PHASE 0: EXTRACTION\n", "=================================================================\n", " Captions: 20,000\n", "\n", " Extracting: bert...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 0:\n", " aux[\"cross_expert_var\"] = cross_features.var(dim=0).mean()\n", " else:\n", " aux[\"cross_expert_var\"] = torch.tensor(0.0, device=emb.device)\n", "\n", " # 6. Bank CV (on geometric context space)\n", " if B >= 10:\n", " ctx_n = F.normalize(geo_context, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = ctx_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " bank_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"bank_cv\"] = bank_cv\n", " else:\n", " aux[\"bank_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # 7. Embedding-space CV (should match consensus target)\n", " if B >= 10:\n", " emb_n = F.normalize(emb, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " emb_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"emb_cv\"] = emb_cv\n", " else:\n", " aux[\"emb_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # Diagnostics\n", " aux[\"expert_cos_mean\"] = expert_cos.mean().item()\n", " aux[\"expert_cos_std\"] = expert_cos.std().item()\n", " aux[\"anchor_max_cos\"] = anchor_cos.max(dim=-1).values.mean().item()\n", " aux[\"anchor_mean_cos\"] = anchor_cos.mean().item()\n", " if cross_features.shape[1] > 0:\n", " aux[\"cross_expert_cos\"] = cross_features.mean().item()\n", "\n", " return enriched, aux\n", "\n", " def bank_loss(self, aux):\n", " \"\"\"\n", " All targets come from measured consensus statistics.\n", " No arbitrary constants.\n", " \"\"\"\n", " loss = (\n", " 1.0 * aux[\"expert_agreement\"] + # experts should agree\n", " 1.0 * aux[\"rotation_ortho\"] + # rotations stay orthogonal\n", " 0.5 * aux[\"anchor_spread\"] + # anchors cover the sphere\n", " 0.1 * aux[\"anchor_entropy\"] + # sharp anchor assignments\n", " 0.3 * aux[\"cross_expert_var\"] + # stable cross-expert structure\n", " 0.3 * (aux[\"bank_cv\"] - self.target_cv).abs() + # bank CV → consensus CV\n", " 0.3 * (aux[\"emb_cv\"] - self.target_cv).abs() # verify emb CV stays at consensus\n", " )\n", " return loss\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.12, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def measure_consensus_stats(consensus_embs, n_check=2000):\n", " \"\"\"Measure exact geometric statistics of the consensus manifold.\"\"\"\n", " embs = consensus_embs[:n_check].float()\n", " # CV\n", " cv = cv_metric(embs.to(DEVICE))\n", " # Pairwise cosine\n", " sim = embs @ embs.T\n", " mask = ~torch.eye(embs.shape[0], dtype=torch.bool)\n", " pairwise = sim[mask]\n", " mean_cos = pairwise.mean().item()\n", " # Spectral\n", " centered = embs - embs.mean(0, keepdim=True)\n", " S = torch.linalg.svdvals(centered)\n", " S_norm = (S / (S.sum() + 1e-8)).tolist()[:50]\n", " # Eff dim\n", " eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " return {\n", " \"cv\": cv,\n", " \"mean_cos\": mean_cos,\n", " \"spectral\": S_norm,\n", " \"eff_dim\": eff_dim,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACTION + ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " N_SAMPLES = 20000\n", " MAX_LEN = 128\n", " BATCH = 256\n", "\n", " # ── Phase 0: Extract ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: EXTRACTION\")\n", " print(f\"{'='*65}\")\n", "\n", " from datasets import load_dataset\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_SAMPLES:\n", " break\n", " print(f\" Captions: {len(captions):,}\")\n", "\n", " embeds = {}\n", " for model_name, short, max_len in EXPERTS:\n", " print(f\"\\n Extracting: {short}...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " all_emb = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, len(captions), 128), desc=f\" {short}\"):\n", " batch = captions[i:i+128]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", " embeds[short] = torch.cat(all_emb)\n", " print(f\" Shape: {embeds[short].shape}\")\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 0b: Align + Consensus + Measure ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0b: PROCRUSTES ALIGNMENT + CONSENSUS STATISTICS\")\n", " print(f\"{'='*65}\")\n", "\n", " ref = \"bert\"\n", " names = [s for _, s, _ in EXPERTS]\n", " procrustes_results = {}\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name], embeds[ref])\n", " procrustes_results[name] = info\n", " aligned[name] = apply_align(embeds[name], info)\n", " print(f\" {name:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}\")\n", "\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " for name in names:\n", " cos = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", " # Measure EXACT consensus statistics — these become the bank's targets\n", " print(f\"\\n Measuring consensus statistics...\")\n", " consensus_stats = measure_consensus_stats(consensus)\n", " print(f\" CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Mean cos: {consensus_stats['mean_cos']:.4f}\")\n", " print(f\" Eff dim: {consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Spectral: [{', '.join(f'{s:.4f}' for s in consensus_stats['spectral'][:5])}...]\")\n", "\n", " del embeds, aligned\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 1: Train Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: TRAIN STUDENT\")\n", " print(f\"{'='*65}\")\n", "\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " tokens = tokenizer(captions, max_length=MAX_LEN, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = tokens[\"input_ids\"]\n", " attention_mask = tokens[\"attention_mask\"]\n", "\n", " n_train = N_SAMPLES - 2000\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " student = MiniStudent(\n", " vocab_size=tokenizer.vocab_size, max_len=MAX_LEN,\n", " d_model=256, n_heads=4, n_layers=4, d_ff=1024,\n", " output_dim=768, dropout=0.1, pad_token_id=tokenizer.pad_token_id\n", " ).to(DEVICE)\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Student: {n_params:,} params\")\n", " print(f\" CV target: {consensus_stats['cv']:.4f}\")\n", "\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=3e-4, weight_decay=0.01)\n", "\n", " for epoch in range(5):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " t_loss, t_acc, t_cos, n = 0, 0, 0, 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=consensus_stats[\"cv\"])\n", " loss = l_nce + l_mse + 0.1 * l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", " t_loss += loss.item(); t_acc += acc; t_cos += cos; n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", " student.eval()\n", " with torch.no_grad():\n", " v_emb = student(val_ids, val_mask)\n", " _, v_acc = infonce(v_emb[:1000], val_targets[:1000])\n", " v_cos = F.cosine_similarity(v_emb, val_targets, dim=-1).mean().item()\n", " v_cv = cv_metric(v_emb[:1000])\n", " print(f\" E{epoch+1}: {elapsed:.0f}s loss={t_loss/d:.4f} \"\n", " f\"t_acc={t_acc/d:.3f} t_cos={t_cos/d:.3f} \"\n", " f\"v_acc={v_acc:.3f} v_cos={v_cos:.3f} v_cv={v_cv:.3f}\")\n", "\n", " torch.save(student.state_dict(), \"mini_student.pt\")\n", " print(f\"\\n Student saved. v_cos={v_cos:.3f}, v_cv={v_cv:.3f}\")\n", "\n", " # ── Phase 2: Train Alignment Bank ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: TRAIN ALIGNMENT BANK (student frozen)\")\n", " print(f\"{'='*65}\")\n", "\n", " student.eval()\n", " for p in student.parameters():\n", " p.requires_grad = False\n", "\n", " print(\" Pre-encoding through frozen student...\")\n", " with torch.no_grad():\n", " all_embs = []\n", " for i in range(0, n_train, 512):\n", " j = min(i + 512, n_train)\n", " emb = student(train_ids[i:j], train_mask[i:j])\n", " all_embs.append(emb)\n", " student_embs = torch.cat(all_embs)\n", " val_student_embs = student(val_ids, val_mask)\n", " print(f\" Student embeddings: {student_embs.shape}\")\n", "\n", " bank = AlignmentBank(\n", " d_embed=768, n_experts=len(EXPERTS),\n", " n_anchors=512, d_bank=128\n", " ).to(DEVICE)\n", "\n", " bank.init_from_procrustes(procrustes_results, names,\n", " consensus[:n_train], consensus_stats)\n", " bank_params = sum(p.numel() for p in bank.parameters())\n", " print(f\" Bank: {bank_params:,} params\")\n", " print(f\" Bank targets: CV={bank.target_cv.item():.4f}, \"\n", " f\"mean_cos={bank.target_mean_cos.item():.4f}\")\n", "\n", " bank_opt = torch.optim.AdamW(bank.parameters(), lr=1e-3, weight_decay=0.01)\n", " BANK_EPOCHS = 20\n", " BANK_BATCH = 256\n", "\n", " for epoch in range(BANK_EPOCHS):\n", " bank.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " stats = {\"expert_agreement\": 0, \"rotation_ortho\": 0,\n", " \"anchor_spread\": 0, \"bank_cv\": 0, \"emb_cv\": 0,\n", " \"cross_expert_var\": 0}\n", " n = 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BANK_BATCH):\n", " idx = perm[i:i+BANK_BATCH]\n", " if len(idx) < 16: continue\n", " emb = student_embs[idx]\n", " enriched, aux = bank(emb)\n", " loss = bank.bank_loss(aux)\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(bank.parameters(), 1.0)\n", " bank_opt.step(); bank_opt.zero_grad(set_to_none=True)\n", " total_loss += loss.item()\n", " for k in stats:\n", " if k in aux:\n", " v = aux[k]\n", " stats[k] += v.item() if torch.is_tensor(v) else v\n", " n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " v_enriched, v_aux = bank(val_student_embs)\n", " v_loss = bank.bank_loss(v_aux).item()\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} v_loss={v_loss:.4f} \"\n", " f\"agr={stats['expert_agreement']/d:.5f} \"\n", " f\"ortho={stats['rotation_ortho']/d:.5f} \"\n", " f\"spread={stats['anchor_spread']/d:.5f} \"\n", " f\"b_cv={stats['bank_cv']/d:.4f} \"\n", " f\"e_cv={stats['emb_cv']/d:.4f} \"\n", " f\"x_var={stats['cross_expert_var']/d:.5f} \"\n", " f\"a_max={v_aux['anchor_max_cos']:.3f} \"\n", " f\"exp={v_aux['expert_cos_mean']:.3f}±{v_aux['expert_cos_std']:.3f}\")\n", "\n", " torch.save(bank.state_dict(), \"alignment_bank.pt\")\n", "\n", " # ── Phase 3: Geometric Verification ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: GEOMETRIC VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " enriched_val, v_aux = bank(val_student_embs)\n", " original_768 = enriched_val[:, :768]\n", " geo_context = enriched_val[:, 768:]\n", "\n", " passthrough_cos = F.cosine_similarity(\n", " original_768[:100], val_student_embs[:100], dim=-1).mean().item()\n", " geo_cv = cv_metric(F.normalize(geo_context[:1000], dim=-1))\n", " S = torch.linalg.svdvals(\n", " geo_context[:1000].float() - geo_context[:1000].float().mean(0))\n", " geo_eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " # Verify consensus stats are preserved\n", " emb_cv = cv_metric(val_student_embs[:1000])\n", "\n", " print(f\" Passthrough: {passthrough_cos:.6f} (target: 1.000)\")\n", " print(f\" Emb CV: {emb_cv:.4f} (consensus: {consensus_stats['cv']:.4f})\")\n", " print(f\" Geo context CV: {geo_cv:.4f}\")\n", " print(f\" Geo eff_dim: {geo_eff_dim:.1f} / {bank.d_bank}\")\n", " print(f\" Expert cos: {v_aux['expert_cos_mean']:.3f} ± {v_aux['expert_cos_std']:.3f}\")\n", " print(f\" Anchor max cos: {v_aux['anchor_max_cos']:.3f}\")\n", " if \"cross_expert_cos\" in v_aux:\n", " print(f\" Cross-expert: {v_aux['cross_expert_cos']:.3f}\")\n", "\n", " # ── Phase 4: Classifier Stability Test ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: CLASSIFIER STABILITY TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " with torch.no_grad():\n", " embs = val_student_embs[:1000]\n", " sim = embs @ embs.T\n", " sim.fill_diagonal_(-1)\n", " n_pairs = 3000\n", " idx_a = torch.randint(0, 1000, (n_pairs,))\n", " idx_b = torch.randint(0, 1000, (n_pairs,))\n", " pair_cos = sim[idx_a, idx_b]\n", " sorted_cos, _ = pair_cos.sort()\n", " t1 = sorted_cos[n_pairs // 3].item()\n", " t2 = sorted_cos[2 * n_pairs // 3].item()\n", " labels = torch.zeros(n_pairs, dtype=torch.long, device=DEVICE)\n", " labels[pair_cos > t2] = 0\n", " labels[(pair_cos <= t2) & (pair_cos > t1)] = 1\n", " labels[pair_cos <= t1] = 2\n", " enriched_a, _ = bank(embs[idx_a])\n", " enriched_b, _ = bank(embs[idx_b])\n", "\n", " for mode in [\"with_bank\", \"without_bank\"]:\n", " if mode == \"with_bank\":\n", " feat_dim = (768 + 128) * 2\n", " features = torch.cat([enriched_a, enriched_b], dim=-1)\n", " else:\n", " feat_dim = 768 * 2\n", " features = torch.cat([embs[idx_a], embs[idx_b]], dim=-1)\n", "\n", " clf = nn.Sequential(\n", " nn.Linear(feat_dim, 256), nn.GELU(), nn.LayerNorm(256),\n", " nn.Linear(256, 3)\n", " ).to(DEVICE)\n", " clf_opt = torch.optim.Adam(clf.parameters(), lr=1e-3)\n", " n_clf_train = 2400\n", " train_f = features[:n_clf_train].detach()\n", " train_l = labels[:n_clf_train]\n", " val_f = features[n_clf_train:].detach()\n", " val_l = labels[n_clf_train:]\n", " for e in range(30):\n", " clf.train()\n", " logits = clf(train_f)\n", " loss = F.cross_entropy(logits, train_l)\n", " loss.backward(); clf_opt.step(); clf_opt.zero_grad()\n", " clf.eval()\n", " with torch.no_grad():\n", " v_acc = (clf(val_f).argmax(-1) == val_l).float().mean().item()\n", " t_acc = (clf(train_f).argmax(-1) == train_l).float().mean().item()\n", " print(f\" {mode:15s}: train={t_acc:.3f} val={v_acc:.3f} gap={t_acc-v_acc:.3f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Consensus CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Consensus eff_dim:{consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Student v_cos: {v_cos:.3f}\")\n", " print(f\" Student v_cv: {v_cv:.3f}\")\n", " print(f\" Bank params: {bank_params:,}\")\n", " print(f\" Bank geo_eff_dim: {geo_eff_dim:.1f}\")\n", " print(f\" Bank geo_cv: {geo_cv:.4f}\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "fb248fb346564862b9c633d195d88d2a", "68cc85aa9433422d925937b245bad649", "ed527cdca7de4f569ae2e879ae2c5cc4", "3926e9cb649844cbaed16d16af96490d", "19dff49386d24a4cb9940d1c41b6d87c", "75912febbe024523ab196ebee515abe3", "a492e1fac92342dab64eac36dd2694ea", "e00e46d477814ff3a15d3a0549cadcb4", "2665800f69444ac5b9f7ba2d70eab01d", "93257a415826429f9de685655370237e", "c01652b8718e4a80b8c92c82a1fc9f49", "5c2fe3ce505c425b8a5133636fa3e59a", "8eaf6eedc46144ea828b8cf1bf3c177a", "9b4aa70316fd4b30acc0a957b2e024dd", "dd4163db08b94b88bbdb6f2e914d91a4", "17938cc8e1184b3ca9db7c72fe5c40fe", "9549dcb9f1604714a906e581101174fc", "493fc58cc1084e00b4aef9e1a4ce7687", "73fdc6a1594c47de86c5124e192bc43f", "6fbf05deedf94711a079726a73811f52", "3f36a310c43c4012a2a0c19294fe0e13", "11b3b34f9ffc4e879152200104225df1" ] }, "id": "KwYHvOk_0FeQ", "outputId": "07319bb1-c7db-4aea-a173-8b54525c6e01" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "RAPID PROTOTYPE v2: Differentiation-Centered Bank\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "PHASE 0: EXTRACTION\n", "=================================================================\n", " Captions: 20,000\n", "\n", " Extracting: bert...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 0:\n", " aux[\"cross_expert_var\"] = cross_features.var(dim=0).mean()\n", " else:\n", " aux[\"cross_expert_var\"] = torch.tensor(0.0, device=emb.device)\n", "\n", " # 6. Bank CV (on geometric context space)\n", " if B >= 10:\n", " ctx_n = F.normalize(geo_context, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = ctx_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " bank_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"bank_cv\"] = bank_cv\n", " else:\n", " aux[\"bank_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # 7. Embedding-space CV (should match consensus target)\n", " if B >= 10:\n", " emb_n = F.normalize(emb, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " emb_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"emb_cv\"] = emb_cv\n", " else:\n", " aux[\"emb_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # Diagnostics\n", " aux[\"expert_cos_mean\"] = expert_cos.mean().item()\n", " aux[\"expert_cos_std\"] = expert_cos.std().item()\n", " aux[\"anchor_max_cos\"] = anchor_cos.max(dim=-1).values.mean().item()\n", " aux[\"anchor_mean_cos\"] = anchor_cos.mean().item()\n", " if cross_features.shape[1] > 0:\n", " aux[\"cross_expert_cos\"] = cross_features.mean().item()\n", "\n", " return enriched, aux\n", "\n", " def bank_loss(self, aux):\n", " \"\"\"\n", " All targets come from measured consensus statistics.\n", " No arbitrary constants.\n", " \"\"\"\n", " loss = (\n", " 1.0 * aux[\"expert_agreement\"] + # experts should agree\n", " 1.0 * aux[\"rotation_ortho\"] + # rotations stay orthogonal\n", " 0.5 * aux[\"anchor_spread\"] + # anchors cover the sphere\n", " 0.1 * aux[\"anchor_entropy\"] + # sharp anchor assignments\n", " 0.3 * aux[\"cross_expert_var\"] + # stable cross-expert structure\n", " 0.3 * (aux[\"bank_cv\"] - self.target_cv).abs() + # bank CV → consensus CV\n", " 0.3 * (aux[\"emb_cv\"] - self.target_cv).abs() # verify emb CV stays at consensus\n", " )\n", " return loss\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.12, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def measure_consensus_stats(consensus_embs, n_check=2000):\n", " \"\"\"Measure exact geometric statistics of the consensus manifold.\"\"\"\n", " embs = consensus_embs[:n_check].float()\n", " # CV\n", " cv = cv_metric(embs.to(DEVICE))\n", " # Pairwise cosine\n", " sim = embs @ embs.T\n", " mask = ~torch.eye(embs.shape[0], dtype=torch.bool)\n", " pairwise = sim[mask]\n", " mean_cos = pairwise.mean().item()\n", " # Spectral\n", " centered = embs - embs.mean(0, keepdim=True)\n", " S = torch.linalg.svdvals(centered)\n", " S_norm = (S / (S.sum() + 1e-8)).tolist()[:50]\n", " # Eff dim\n", " eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " return {\n", " \"cv\": cv,\n", " \"mean_cos\": mean_cos,\n", " \"spectral\": S_norm,\n", " \"eff_dim\": eff_dim,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACTION + ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " N_SAMPLES = 20000\n", " MAX_LEN = 128\n", " BATCH = 256\n", "\n", " # ── Phase 0: Extract ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: EXTRACTION\")\n", " print(f\"{'='*65}\")\n", "\n", " from datasets import load_dataset\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_SAMPLES:\n", " break\n", " print(f\" Captions: {len(captions):,}\")\n", "\n", " embeds = {}\n", " for model_name, short, max_len in EXPERTS:\n", " print(f\"\\n Extracting: {short}...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " all_emb = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, len(captions), 128), desc=f\" {short}\"):\n", " batch = captions[i:i+128]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", " embeds[short] = torch.cat(all_emb)\n", " print(f\" Shape: {embeds[short].shape}\")\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 0b: Align + Consensus + Measure ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0b: PROCRUSTES ALIGNMENT + CONSENSUS STATISTICS\")\n", " print(f\"{'='*65}\")\n", "\n", " ref = \"bert\"\n", " names = [s for _, s, _ in EXPERTS]\n", " procrustes_results = {}\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name], embeds[ref])\n", " procrustes_results[name] = info\n", " aligned[name] = apply_align(embeds[name], info)\n", " print(f\" {name:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}\")\n", "\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " for name in names:\n", " cos = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", " # Measure EXACT consensus statistics — these become the bank's targets\n", " print(f\"\\n Measuring consensus statistics...\")\n", " consensus_stats = measure_consensus_stats(consensus)\n", " print(f\" CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Mean cos: {consensus_stats['mean_cos']:.4f}\")\n", " print(f\" Eff dim: {consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Spectral: [{', '.join(f'{s:.4f}' for s in consensus_stats['spectral'][:5])}...]\")\n", "\n", " del embeds, aligned\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 1: Train Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: TRAIN STUDENT\")\n", " print(f\"{'='*65}\")\n", "\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " tokens = tokenizer(captions, max_length=MAX_LEN, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = tokens[\"input_ids\"]\n", " attention_mask = tokens[\"attention_mask\"]\n", "\n", " n_train = N_SAMPLES - 2000\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " student = MiniStudent(\n", " vocab_size=tokenizer.vocab_size, max_len=MAX_LEN,\n", " d_model=256, n_heads=4, n_layers=4, d_ff=1024,\n", " output_dim=768, dropout=0.1, pad_token_id=tokenizer.pad_token_id\n", " ).to(DEVICE)\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Student: {n_params:,} params\")\n", " print(f\" CV target: {consensus_stats['cv']:.4f}\")\n", "\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=3e-4, weight_decay=0.01)\n", "\n", " for epoch in range(5):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " t_loss, t_acc, t_cos, n = 0, 0, 0, 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=consensus_stats[\"cv\"])\n", " loss = l_nce + l_mse + 0.1 * l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", " t_loss += loss.item(); t_acc += acc; t_cos += cos; n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", " student.eval()\n", " with torch.no_grad():\n", " v_emb = student(val_ids, val_mask)\n", " _, v_acc = infonce(v_emb[:1000], val_targets[:1000])\n", " v_cos = F.cosine_similarity(v_emb, val_targets, dim=-1).mean().item()\n", " v_cv = cv_metric(v_emb[:1000])\n", " print(f\" E{epoch+1}: {elapsed:.0f}s loss={t_loss/d:.4f} \"\n", " f\"t_acc={t_acc/d:.3f} t_cos={t_cos/d:.3f} \"\n", " f\"v_acc={v_acc:.3f} v_cos={v_cos:.3f} v_cv={v_cv:.3f}\")\n", "\n", " torch.save(student.state_dict(), \"mini_student.pt\")\n", " print(f\"\\n Student saved. v_cos={v_cos:.3f}, v_cv={v_cv:.3f}\")\n", "\n", " # ── Phase 2: Train Alignment Bank ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: TRAIN ALIGNMENT BANK (student frozen)\")\n", " print(f\"{'='*65}\")\n", "\n", " student.eval()\n", " for p in student.parameters():\n", " p.requires_grad = False\n", "\n", " print(\" Pre-encoding through frozen student...\")\n", " with torch.no_grad():\n", " all_embs = []\n", " for i in range(0, n_train, 512):\n", " j = min(i + 512, n_train)\n", " emb = student(train_ids[i:j], train_mask[i:j])\n", " all_embs.append(emb)\n", " student_embs = torch.cat(all_embs)\n", " val_student_embs = student(val_ids, val_mask)\n", " print(f\" Student embeddings: {student_embs.shape}\")\n", "\n", " bank = AlignmentBank(\n", " d_embed=768, n_experts=len(EXPERTS),\n", " n_anchors=512, d_bank=128\n", " ).to(DEVICE)\n", "\n", " bank.init_from_procrustes(procrustes_results, names,\n", " consensus[:n_train], consensus_stats)\n", " bank_params = sum(p.numel() for p in bank.parameters())\n", " print(f\" Bank: {bank_params:,} params\")\n", " print(f\" Bank targets: CV={bank.target_cv.item():.4f}, \"\n", " f\"mean_cos={bank.target_mean_cos.item():.4f}\")\n", "\n", " bank_opt = torch.optim.AdamW(bank.parameters(), lr=1e-3, weight_decay=0.01)\n", " BANK_EPOCHS = 20\n", " BANK_BATCH = 256\n", "\n", " for epoch in range(BANK_EPOCHS):\n", " bank.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " stats = {\"expert_agreement\": 0, \"rotation_ortho\": 0,\n", " \"anchor_spread\": 0, \"bank_cv\": 0, \"emb_cv\": 0,\n", " \"cross_expert_var\": 0}\n", " n = 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BANK_BATCH):\n", " idx = perm[i:i+BANK_BATCH]\n", " if len(idx) < 16: continue\n", " emb = student_embs[idx]\n", " enriched, aux = bank(emb)\n", " loss = bank.bank_loss(aux)\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(bank.parameters(), 1.0)\n", " bank_opt.step(); bank_opt.zero_grad(set_to_none=True)\n", " total_loss += loss.item()\n", " for k in stats:\n", " if k in aux:\n", " v = aux[k]\n", " stats[k] += v.item() if torch.is_tensor(v) else v\n", " n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " v_enriched, v_aux = bank(val_student_embs)\n", " v_loss = bank.bank_loss(v_aux).item()\n", "\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} v_loss={v_loss:.4f} \"\n", " f\"agr={stats['expert_agreement']/d:.5f} \"\n", " f\"ortho={stats['rotation_ortho']/d:.5f} \"\n", " f\"spread={stats['anchor_spread']/d:.5f} \"\n", " f\"b_cv={stats['bank_cv']/d:.4f} \"\n", " f\"e_cv={stats['emb_cv']/d:.4f} \"\n", " f\"x_var={stats['cross_expert_var']/d:.5f} \"\n", " f\"a_max={v_aux['anchor_max_cos']:.3f} \"\n", " f\"exp={v_aux['expert_cos_mean']:.3f}±{v_aux['expert_cos_std']:.3f}\")\n", "\n", " torch.save(bank.state_dict(), \"alignment_bank.pt\")\n", "\n", " # ── Phase 3: Geometric Verification ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: GEOMETRIC VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " enriched_val, v_aux = bank(val_student_embs)\n", " original_768 = enriched_val[:, :768]\n", " geo_context = enriched_val[:, 768:]\n", "\n", " passthrough_cos = F.cosine_similarity(\n", " original_768[:100], val_student_embs[:100], dim=-1).mean().item()\n", " geo_cv = cv_metric(F.normalize(geo_context[:1000], dim=-1))\n", " S = torch.linalg.svdvals(\n", " geo_context[:1000].float() - geo_context[:1000].float().mean(0))\n", " geo_eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " # Verify consensus stats are preserved\n", " emb_cv = cv_metric(val_student_embs[:1000])\n", "\n", " print(f\" Passthrough: {passthrough_cos:.6f} (target: 1.000)\")\n", " print(f\" Emb CV: {emb_cv:.4f} (consensus: {consensus_stats['cv']:.4f})\")\n", " print(f\" Geo context CV: {geo_cv:.4f}\")\n", " print(f\" Geo eff_dim: {geo_eff_dim:.1f} / {bank.d_bank}\")\n", " print(f\" Expert cos: {v_aux['expert_cos_mean']:.3f} ± {v_aux['expert_cos_std']:.3f}\")\n", " print(f\" Anchor max cos: {v_aux['anchor_max_cos']:.3f}\")\n", " if \"cross_expert_cos\" in v_aux:\n", " print(f\" Cross-expert: {v_aux['cross_expert_cos']:.3f}\")\n", "\n", " # ── Phase 4: Classifier Stability Test ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: CLASSIFIER STABILITY TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " with torch.no_grad():\n", " embs = val_student_embs[:1000]\n", " sim = embs @ embs.T\n", " sim.fill_diagonal_(-1)\n", " n_pairs = 3000\n", " idx_a = torch.randint(0, 1000, (n_pairs,))\n", " idx_b = torch.randint(0, 1000, (n_pairs,))\n", " pair_cos = sim[idx_a, idx_b]\n", " sorted_cos, _ = pair_cos.sort()\n", " t1 = sorted_cos[n_pairs // 3].item()\n", " t2 = sorted_cos[2 * n_pairs // 3].item()\n", " labels = torch.zeros(n_pairs, dtype=torch.long, device=DEVICE)\n", " labels[pair_cos > t2] = 0\n", " labels[(pair_cos <= t2) & (pair_cos > t1)] = 1\n", " labels[pair_cos <= t1] = 2\n", " enriched_a, _ = bank(embs[idx_a])\n", " enriched_b, _ = bank(embs[idx_b])\n", "\n", " for mode in [\"with_bank\", \"without_bank\"]:\n", " if mode == \"with_bank\":\n", " feat_dim = (768 + 128) * 2\n", " features = torch.cat([enriched_a, enriched_b], dim=-1)\n", " else:\n", " feat_dim = 768 * 2\n", " features = torch.cat([embs[idx_a], embs[idx_b]], dim=-1)\n", "\n", " clf = nn.Sequential(\n", " nn.Linear(feat_dim, 256), nn.GELU(), nn.LayerNorm(256),\n", " nn.Linear(256, 3)\n", " ).to(DEVICE)\n", " clf_opt = torch.optim.Adam(clf.parameters(), lr=1e-3)\n", " n_clf_train = 2400\n", " train_f = features[:n_clf_train].detach()\n", " train_l = labels[:n_clf_train]\n", " val_f = features[n_clf_train:].detach()\n", " val_l = labels[n_clf_train:]\n", " for e in range(30):\n", " clf.train()\n", " logits = clf(train_f)\n", " loss = F.cross_entropy(logits, train_l)\n", " loss.backward(); clf_opt.step(); clf_opt.zero_grad()\n", " clf.eval()\n", " with torch.no_grad():\n", " v_acc = (clf(val_f).argmax(-1) == val_l).float().mean().item()\n", " t_acc = (clf(train_f).argmax(-1) == train_l).float().mean().item()\n", " print(f\" {mode:15s}: train={t_acc:.3f} val={v_acc:.3f} gap={t_acc-v_acc:.3f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Consensus CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Consensus eff_dim:{consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Student v_cos: {v_cos:.3f}\")\n", " print(f\" Student v_cv: {v_cv:.3f}\")\n", " print(f\" Bank params: {bank_params:,}\")\n", " print(f\" Bank geo_eff_dim: {geo_eff_dim:.1f}\")\n", " print(f\" Bank geo_cv: {geo_cv:.4f}\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "96e6b188456a440491abf1b9f97e0f1b", "2bbf292baba24d83bc6969f17945ebdf", "bfbcf1c77d2e4c6dab61ecdb8b32f45b", "c9380e44797d4631bacdce608af8501d", "e4d3027a8ed1412693b1e8dd601c06ab", "f63ba9edd18d483795ebaa2ac160dbed", "2d01e0821f704e09945bcd17419c11e2", "e89e69591d21429fa8007b0fa576d61c", "5cdba3bf3b5f4d259ebd63ecab967fc2", "169c779b68ca4da6a16eb88b87e0cf62", "c4bdcb3665724031a1e7f1f06cfd496e", "1be48dcc1d5a44af98283079bf2e251c", "d00dd42d3bd5429d86f33edaa9a85b8b", "c219a9706b674b7aa89c4d5bc11e6df3", "1f772e7517fe4ef5bbcc657a384d37e8", "eadbb240982c4fb6aa19bf61ae99c672", "b06994fee46d41d7930ce03d439a1ae9", "0c37b804865244e9b4db2aac3e277a6e", "3284c3d7ea4342dbae5387da632d73f4", "3bbc747f5ccf4acab6ae9c38506fef28", "4af6797d8e78453982f35694c60785ef", "a1f0417b8f464996b1c93dc219c3d22f" ] }, "id": "YmyhkFVO1nx0", "outputId": "07471f82-6a7d-46c9-a95b-c3811b59273d" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "RAPID PROTOTYPE v2: Differentiation-Centered Bank\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "PHASE 0: EXTRACTION\n", "=================================================================\n", " Captions: 20,000\n", "\n", " Extracting: bert...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 0:\n", " aux[\"cross_expert_var\"] = cross_features.var(dim=0).mean()\n", " else:\n", " aux[\"cross_expert_var\"] = torch.tensor(0.0, device=emb.device)\n", "\n", " # 6. Disagreement preservation\n", " # The distribution of disagreement should stay at the measured target\n", " batch_cross_mean = cross_features.mean() if cross_features.shape[1] > 0 else torch.tensor(0.0, device=emb.device)\n", " batch_cross_std = cross_features.std() if cross_features.shape[1] > 0 else torch.tensor(0.0, device=emb.device)\n", " batch_disagree_ratio = disagreement_ratio.mean()\n", " aux[\"disagree_preserve\"] = (\n", " (batch_cross_mean - self.target_cross_cos_mean).pow(2) +\n", " (batch_cross_std - self.target_cross_cos_std).pow(2) +\n", " (batch_disagree_ratio - self.target_disagreement_ratio).pow(2)\n", " )\n", "\n", " # 7. Bank CV\n", " if B >= 10:\n", " ctx_n = F.normalize(geo_context, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = ctx_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " bank_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"bank_cv\"] = bank_cv\n", " else:\n", " aux[\"bank_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # 8. Emb CV\n", " if B >= 10:\n", " emb_n = F.normalize(emb, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " emb_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"emb_cv\"] = emb_cv\n", " else:\n", " aux[\"emb_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # Diagnostics\n", " aux[\"expert_cos_mean\"] = expert_cos.mean().item()\n", " aux[\"expert_cos_std\"] = expert_cos.std().item()\n", " aux[\"anchor_max_cos\"] = anchor_cos.max(dim=-1).values.mean().item()\n", " aux[\"anchor_mean_cos\"] = anchor_cos.mean().item()\n", " if cross_features.shape[1] > 0:\n", " aux[\"cross_expert_cos\"] = cross_features.mean().item()\n", " aux[\"cross_expert_cos_std\"] = cross_features.std().item()\n", " aux[\"disagreement_ratio\"] = disagreement_ratio.mean().item()\n", " aux[\"norm_ratio_spread\"] = norm_ratio.std(dim=-1).mean().item()\n", "\n", " return enriched, aux\n", "\n", " def bank_loss(self, aux):\n", " \"\"\"All targets from measured consensus. Preserves disagreement structure.\"\"\"\n", " loss = (\n", " 1.0 * aux[\"expert_agreement\"] +\n", " 1.0 * aux[\"rotation_ortho\"] +\n", " 0.5 * aux[\"anchor_spread\"] +\n", " 0.1 * aux[\"anchor_entropy\"] +\n", " 0.3 * aux[\"cross_expert_var\"] +\n", " 0.3 * (aux[\"bank_cv\"] - self.target_cv).abs() +\n", " 0.3 * (aux[\"emb_cv\"] - self.target_cv).abs() +\n", " 0.5 * aux[\"disagree_preserve\"] # preserve the disagreement distribution\n", " )\n", " return loss\n", "\n", " @torch.no_grad()\n", " def calibrate_disagreement(self, embeddings):\n", " \"\"\"\n", " Measure the initial disagreement structure and store as targets.\n", " Call ONCE after init, before training.\n", " \"\"\"\n", " _, aux = self.forward(embeddings)\n", " if \"cross_expert_cos\" in aux:\n", " self.target_cross_cos_mean.fill_(aux[\"cross_expert_cos\"])\n", " if \"cross_expert_cos_std\" in aux:\n", " self.target_cross_cos_std.fill_(aux[\"cross_expert_cos_std\"])\n", " self.target_disagreement_ratio.fill_(aux[\"disagreement_ratio\"])\n", " print(f\" Calibrated disagreement:\")\n", " print(f\" cross_cos: {self.target_cross_cos_mean.item():.4f} ± {self.target_cross_cos_std.item():.4f}\")\n", " print(f\" disagree_ratio: {self.target_disagreement_ratio.item():.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.12, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def measure_consensus_stats(consensus_embs, n_check=2000):\n", " \"\"\"Measure exact geometric statistics of the consensus manifold.\"\"\"\n", " embs = consensus_embs[:n_check].float()\n", " # CV\n", " cv = cv_metric(embs.to(DEVICE))\n", " # Pairwise cosine\n", " sim = embs @ embs.T\n", " mask = ~torch.eye(embs.shape[0], dtype=torch.bool)\n", " pairwise = sim[mask]\n", " mean_cos = pairwise.mean().item()\n", " # Spectral\n", " centered = embs - embs.mean(0, keepdim=True)\n", " S = torch.linalg.svdvals(centered)\n", " S_norm = (S / (S.sum() + 1e-8)).tolist()[:50]\n", " # Eff dim\n", " eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " return {\n", " \"cv\": cv,\n", " \"mean_cos\": mean_cos,\n", " \"spectral\": S_norm,\n", " \"eff_dim\": eff_dim,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACTION + ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " N_SAMPLES = 20000\n", " MAX_LEN = 128\n", " BATCH = 256\n", "\n", " # ── Phase 0: Extract ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: EXTRACTION\")\n", " print(f\"{'='*65}\")\n", "\n", " from datasets import load_dataset\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_SAMPLES:\n", " break\n", " print(f\" Captions: {len(captions):,}\")\n", "\n", " embeds = {}\n", " for model_name, short, max_len in EXPERTS:\n", " print(f\"\\n Extracting: {short}...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " all_emb = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, len(captions), 128), desc=f\" {short}\"):\n", " batch = captions[i:i+128]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", " embeds[short] = torch.cat(all_emb)\n", " print(f\" Shape: {embeds[short].shape}\")\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 0b: Align + Consensus + Measure ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0b: PROCRUSTES ALIGNMENT + CONSENSUS STATISTICS\")\n", " print(f\"{'='*65}\")\n", "\n", " ref = \"bert\"\n", " names = [s for _, s, _ in EXPERTS]\n", " procrustes_results = {}\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name], embeds[ref])\n", " procrustes_results[name] = info\n", " aligned[name] = apply_align(embeds[name], info)\n", " print(f\" {name:10s}: cos {info['cos_before']:.4f} → {info['cos_after']:.4f}\")\n", "\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " for name in names:\n", " cos = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", " # Measure EXACT consensus statistics — these become the bank's targets\n", " print(f\"\\n Measuring consensus statistics...\")\n", " consensus_stats = measure_consensus_stats(consensus)\n", " print(f\" CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Mean cos: {consensus_stats['mean_cos']:.4f}\")\n", " print(f\" Eff dim: {consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Spectral: [{', '.join(f'{s:.4f}' for s in consensus_stats['spectral'][:5])}...]\")\n", "\n", " del embeds, aligned\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 1: Train Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: TRAIN STUDENT\")\n", " print(f\"{'='*65}\")\n", "\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " tokens = tokenizer(captions, max_length=MAX_LEN, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = tokens[\"input_ids\"]\n", " attention_mask = tokens[\"attention_mask\"]\n", "\n", " n_train = N_SAMPLES - 2000\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " student = MiniStudent(\n", " vocab_size=tokenizer.vocab_size, max_len=MAX_LEN,\n", " d_model=256, n_heads=4, n_layers=4, d_ff=1024,\n", " output_dim=768, dropout=0.1, pad_token_id=tokenizer.pad_token_id\n", " ).to(DEVICE)\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Student: {n_params:,} params\")\n", " print(f\" CV target: {consensus_stats['cv']:.4f}\")\n", "\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=3e-4, weight_decay=0.01)\n", "\n", " for epoch in range(5):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " t_loss, t_acc, t_cos, n = 0, 0, 0, 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=consensus_stats[\"cv\"])\n", " loss = l_nce + l_mse + 0.1 * l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", " t_loss += loss.item(); t_acc += acc; t_cos += cos; n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", " student.eval()\n", " with torch.no_grad():\n", " v_emb = student(val_ids, val_mask)\n", " _, v_acc = infonce(v_emb[:1000], val_targets[:1000])\n", " v_cos = F.cosine_similarity(v_emb, val_targets, dim=-1).mean().item()\n", " v_cv = cv_metric(v_emb[:1000])\n", " print(f\" E{epoch+1}: {elapsed:.0f}s loss={t_loss/d:.4f} \"\n", " f\"t_acc={t_acc/d:.3f} t_cos={t_cos/d:.3f} \"\n", " f\"v_acc={v_acc:.3f} v_cos={v_cos:.3f} v_cv={v_cv:.3f}\")\n", "\n", " torch.save(student.state_dict(), \"mini_student.pt\")\n", " print(f\"\\n Student saved. v_cos={v_cos:.3f}, v_cv={v_cv:.3f}\")\n", "\n", " # ── Phase 2: Train Alignment Bank ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: TRAIN ALIGNMENT BANK (student frozen)\")\n", " print(f\"{'='*65}\")\n", "\n", " student.eval()\n", " for p in student.parameters():\n", " p.requires_grad = False\n", "\n", " print(\" Pre-encoding through frozen student...\")\n", " with torch.no_grad():\n", " all_embs = []\n", " for i in range(0, n_train, 512):\n", " j = min(i + 512, n_train)\n", " emb = student(train_ids[i:j], train_mask[i:j])\n", " all_embs.append(emb)\n", " student_embs = torch.cat(all_embs)\n", " val_student_embs = student(val_ids, val_mask)\n", " print(f\" Student embeddings: {student_embs.shape}\")\n", "\n", " bank = AlignmentBank(\n", " d_embed=768, n_experts=len(EXPERTS),\n", " n_anchors=512, d_bank=128\n", " ).to(DEVICE)\n", "\n", " bank.init_from_procrustes(procrustes_results, names,\n", " consensus[:n_train], consensus_stats)\n", " bank_params = sum(p.numel() for p in bank.parameters())\n", " print(f\" Bank: {bank_params:,} params\")\n", " print(f\" Bank targets: CV={bank.target_cv.item():.4f}, \"\n", " f\"mean_cos={bank.target_mean_cos.item():.4f}\")\n", "\n", " # Calibrate disagreement from initial state (before any training)\n", " bank.calibrate_disagreement(student_embs[:2000])\n", "\n", " bank_opt = torch.optim.AdamW(bank.parameters(), lr=1e-3, weight_decay=0.01)\n", " BANK_EPOCHS = 20\n", " BANK_BATCH = 256\n", "\n", " for epoch in range(BANK_EPOCHS):\n", " bank.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " stats = {\"expert_agreement\": 0, \"rotation_ortho\": 0,\n", " \"anchor_spread\": 0, \"bank_cv\": 0, \"emb_cv\": 0,\n", " \"cross_expert_var\": 0, \"disagree_preserve\": 0}\n", " n = 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BANK_BATCH):\n", " idx = perm[i:i+BANK_BATCH]\n", " if len(idx) < 16: continue\n", " emb = student_embs[idx]\n", " enriched, aux = bank(emb)\n", " loss = bank.bank_loss(aux)\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(bank.parameters(), 1.0)\n", " bank_opt.step(); bank_opt.zero_grad(set_to_none=True)\n", " total_loss += loss.item()\n", " for k in stats:\n", " if k in aux:\n", " v = aux[k]\n", " stats[k] += v.item() if torch.is_tensor(v) else v\n", " n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " v_enriched, v_aux = bank(val_student_embs)\n", " v_loss = bank.bank_loss(v_aux).item()\n", "\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} v_loss={v_loss:.4f}\")\n", " print(f\" Geometry: b_cv={stats['bank_cv']/d:.4f} e_cv={stats['emb_cv']/d:.4f} \"\n", " f\"spread={stats['anchor_spread']/d:.5f} a_max={v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Experts: cos={v_aux['expert_cos_mean']:.3f}±{v_aux['expert_cos_std']:.3f} \"\n", " f\"agr={stats['expert_agreement']/d:.6f} ortho={stats['rotation_ortho']/d:.6f}\")\n", " print(f\" Disagree: x_cos={v_aux.get('cross_expert_cos', 0):.4f}±{v_aux.get('cross_expert_cos_std', 0):.4f} \"\n", " f\"ratio={v_aux['disagreement_ratio']:.6f} \"\n", " f\"preserve={stats['disagree_preserve']/d:.6f} \"\n", " f\"norms={v_aux['norm_ratio_spread']:.4f}\")\n", "\n", " torch.save(bank.state_dict(), \"alignment_bank.pt\")\n", "\n", " # ── Phase 3: Geometric Verification ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: GEOMETRIC VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " enriched_val, v_aux = bank(val_student_embs)\n", " original_768 = enriched_val[:, :768]\n", " geo_context = enriched_val[:, 768:]\n", "\n", " passthrough_cos = F.cosine_similarity(\n", " original_768[:100], val_student_embs[:100], dim=-1).mean().item()\n", " geo_cv = cv_metric(F.normalize(geo_context[:1000], dim=-1))\n", " S = torch.linalg.svdvals(\n", " geo_context[:1000].float() - geo_context[:1000].float().mean(0))\n", " geo_eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " # Verify consensus stats are preserved\n", " emb_cv = cv_metric(val_student_embs[:1000])\n", "\n", " print(f\" Passthrough: {passthrough_cos:.6f} (target: 1.000)\")\n", " print(f\" Emb CV: {emb_cv:.4f} (consensus: {consensus_stats['cv']:.4f})\")\n", " print(f\" Geo context CV: {geo_cv:.4f}\")\n", " print(f\" Geo eff_dim: {geo_eff_dim:.1f} / {bank.d_bank}\")\n", " print(f\" Expert cos: {v_aux['expert_cos_mean']:.3f} ± {v_aux['expert_cos_std']:.3f}\")\n", " print(f\" Anchor max cos: {v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Disagreement:\")\n", " print(f\" Cross-expert: {v_aux.get('cross_expert_cos', 0):.4f} ± {v_aux.get('cross_expert_cos_std', 0):.4f}\")\n", " print(f\" Ratio: {v_aux['disagreement_ratio']:.6f} (target: {bank.target_disagreement_ratio.item():.6f})\")\n", " print(f\" Norm spread: {v_aux['norm_ratio_spread']:.4f}\")\n", "\n", " # ── Phase 4: Classifier Stability Test ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: CLASSIFIER STABILITY TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " with torch.no_grad():\n", " embs = val_student_embs[:1000]\n", " sim = embs @ embs.T\n", " sim.fill_diagonal_(-1)\n", " n_pairs = 3000\n", " idx_a = torch.randint(0, 1000, (n_pairs,))\n", " idx_b = torch.randint(0, 1000, (n_pairs,))\n", " pair_cos = sim[idx_a, idx_b]\n", " sorted_cos, _ = pair_cos.sort()\n", " t1 = sorted_cos[n_pairs // 3].item()\n", " t2 = sorted_cos[2 * n_pairs // 3].item()\n", " labels = torch.zeros(n_pairs, dtype=torch.long, device=DEVICE)\n", " labels[pair_cos > t2] = 0\n", " labels[(pair_cos <= t2) & (pair_cos > t1)] = 1\n", " labels[pair_cos <= t1] = 2\n", " enriched_a, _ = bank(embs[idx_a])\n", " enriched_b, _ = bank(embs[idx_b])\n", "\n", " for mode in [\"with_bank\", \"without_bank\"]:\n", " if mode == \"with_bank\":\n", " feat_dim = (768 + 128) * 2\n", " features = torch.cat([enriched_a, enriched_b], dim=-1)\n", " else:\n", " feat_dim = 768 * 2\n", " features = torch.cat([embs[idx_a], embs[idx_b]], dim=-1)\n", "\n", " clf = nn.Sequential(\n", " nn.Linear(feat_dim, 256), nn.GELU(), nn.LayerNorm(256),\n", " nn.Linear(256, 3)\n", " ).to(DEVICE)\n", " clf_opt = torch.optim.Adam(clf.parameters(), lr=1e-3)\n", " n_clf_train = 2400\n", " train_f = features[:n_clf_train].detach()\n", " train_l = labels[:n_clf_train]\n", " val_f = features[n_clf_train:].detach()\n", " val_l = labels[n_clf_train:]\n", " for e in range(30):\n", " clf.train()\n", " logits = clf(train_f)\n", " loss = F.cross_entropy(logits, train_l)\n", " loss.backward(); clf_opt.step(); clf_opt.zero_grad()\n", " clf.eval()\n", " with torch.no_grad():\n", " v_acc = (clf(val_f).argmax(-1) == val_l).float().mean().item()\n", " t_acc = (clf(train_f).argmax(-1) == train_l).float().mean().item()\n", " print(f\" {mode:15s}: train={t_acc:.3f} val={v_acc:.3f} gap={t_acc-v_acc:.3f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Consensus CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Consensus eff_dim:{consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Student v_cos: {v_cos:.3f}\")\n", " print(f\" Student v_cv: {v_cv:.3f}\")\n", " print(f\" Bank params: {bank_params:,}\")\n", " print(f\" Bank geo_eff_dim: {geo_eff_dim:.1f}\")\n", " print(f\" Bank geo_cv: {geo_cv:.4f}\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "3fc59d54447c48cf95c06fa0b7c44f81", "97a9f9336df3492187221ef28aa2be6c", "44a2bc1c6841464ab8a914aa7907f0e9", "2b914a27903c485faa75b8f755bffe6e", "4caeb796ecc642148f1921227a0ffa76", "e892f44899514757af1188a6e80b7382", "5b31210bff804a90802064e36ed51938", "a46ef317e76345dda61ce0ce5eb61181", "650619cadc124853993e836b3fae340e", "df54dc7f2cfb496d923f85aa9aa719f7", "9258ee70426b4117ae3ae148f6332581", "9faa8b9c10da45b6b3088d2246dab05b", "926ffa40ae224a61927901a0b7524251", "27802385da91475ab1b765fee5519d87", "7265f91c04194da3a4abf7c7b8bedff2", "c2c5c0a854ee477680bdffb98b0d8a89", "75a32209b14e466ab66498a2a7894b28", "ae3a834c74d34f7e842764df0ea2ae44", "41a228039e1843dc92e771715040d525", "b2c514fc44ba465bb6d77c6097f3ddf7", "760d3b2292ba4f67a9927387024169d7", "7da5505ebc93451a81f4c5b7b8a72ee1" ] }, "id": "xXlp5Wh05pmM", "outputId": "2ea3d4a5-de09-4995-aa52-cc93c65306fd" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "RAPID PROTOTYPE v2: Differentiation-Centered Bank\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "PHASE 0: EXTRACTION\n", "=================================================================\n", " Captions: 20,000\n", "\n", " Extracting: bert...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 0:\n", " aux[\"cross_expert_var\"] = cross_features.var(dim=0).mean()\n", " else:\n", " aux[\"cross_expert_var\"] = torch.tensor(0.0, device=emb.device)\n", "\n", " # 6. Disagreement preservation\n", " # The distribution of disagreement should stay at the measured target\n", " batch_cross_mean = cross_features.mean() if cross_features.shape[1] > 0 else torch.tensor(0.0, device=emb.device)\n", " batch_cross_std = cross_features.std() if cross_features.shape[1] > 0 else torch.tensor(0.0, device=emb.device)\n", " batch_disagree_ratio = disagreement_ratio.mean()\n", " aux[\"disagree_preserve\"] = (\n", " (batch_cross_mean - self.target_cross_cos_mean).pow(2) +\n", " (batch_cross_std - self.target_cross_cos_std).pow(2) +\n", " (batch_disagree_ratio - self.target_disagreement_ratio).pow(2)\n", " )\n", "\n", " # 7. Bank CV\n", " if B >= 10:\n", " ctx_n = F.normalize(geo_context, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = ctx_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " bank_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"bank_cv\"] = bank_cv\n", " else:\n", " aux[\"bank_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # 8. Emb CV\n", " if B >= 10:\n", " emb_n = F.normalize(emb, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " emb_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"emb_cv\"] = emb_cv\n", " else:\n", " aux[\"emb_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # Diagnostics\n", " aux[\"expert_cos_mean\"] = expert_cos.mean().item()\n", " aux[\"expert_cos_std\"] = expert_cos.std().item()\n", " aux[\"anchor_max_cos\"] = anchor_cos.max(dim=-1).values.mean().item()\n", " aux[\"anchor_mean_cos\"] = anchor_cos.mean().item()\n", " if cross_features.shape[1] > 0:\n", " aux[\"cross_expert_cos\"] = cross_features.mean().item()\n", " aux[\"cross_expert_cos_std\"] = cross_features.std().item()\n", " aux[\"disagreement_ratio\"] = disagreement_ratio.mean().item()\n", " aux[\"norm_ratio_spread\"] = norm_ratio.std(dim=-1).mean().item()\n", "\n", " return enriched, aux\n", "\n", " def bank_loss(self, aux):\n", " \"\"\"All targets from measured consensus. Preserves disagreement structure.\"\"\"\n", " loss = (\n", " 1.0 * aux[\"expert_agreement\"] +\n", " 1.0 * aux[\"rotation_ortho\"] +\n", " 0.5 * aux[\"anchor_spread\"] +\n", " 0.1 * aux[\"anchor_entropy\"] +\n", " 0.3 * aux[\"cross_expert_var\"] +\n", " 0.3 * (aux[\"bank_cv\"] - self.target_cv).abs() +\n", " 0.3 * (aux[\"emb_cv\"] - self.target_cv).abs() +\n", " 0.5 * aux[\"disagree_preserve\"] # preserve the disagreement distribution\n", " )\n", " return loss\n", "\n", " @torch.no_grad()\n", " def calibrate_disagreement(self, embeddings):\n", " \"\"\"\n", " Measure the initial disagreement structure and store as targets.\n", " Call ONCE after init, before training.\n", " \"\"\"\n", " _, aux = self.forward(embeddings)\n", " if \"cross_expert_cos\" in aux:\n", " self.target_cross_cos_mean.fill_(aux[\"cross_expert_cos\"])\n", " if \"cross_expert_cos_std\" in aux:\n", " self.target_cross_cos_std.fill_(aux[\"cross_expert_cos_std\"])\n", " self.target_disagreement_ratio.fill_(aux[\"disagreement_ratio\"])\n", " print(f\" Calibrated disagreement:\")\n", " print(f\" cross_cos: {self.target_cross_cos_mean.item():.4f} ± {self.target_cross_cos_std.item():.4f}\")\n", " print(f\" disagree_ratio: {self.target_disagreement_ratio.item():.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.12, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def measure_consensus_stats(consensus_embs, n_check=2000):\n", " \"\"\"Measure exact geometric statistics of the consensus manifold.\"\"\"\n", " embs = consensus_embs[:n_check].float()\n", " # CV\n", " cv = cv_metric(embs.to(DEVICE))\n", " # Pairwise cosine\n", " sim = embs @ embs.T\n", " mask = ~torch.eye(embs.shape[0], dtype=torch.bool)\n", " pairwise = sim[mask]\n", " mean_cos = pairwise.mean().item()\n", " # Spectral\n", " centered = embs - embs.mean(0, keepdim=True)\n", " S = torch.linalg.svdvals(centered)\n", " S_norm = (S / (S.sum() + 1e-8)).tolist()[:50]\n", " # Eff dim\n", " eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " return {\n", " \"cv\": cv,\n", " \"mean_cos\": mean_cos,\n", " \"spectral\": S_norm,\n", " \"eff_dim\": eff_dim,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACTION + ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " N_SAMPLES = 20000\n", " MAX_LEN = 128\n", " BATCH = 256\n", "\n", " # ── Phase 0: Extract ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: EXTRACTION\")\n", " print(f\"{'='*65}\")\n", "\n", " from datasets import load_dataset\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_SAMPLES:\n", " break\n", " print(f\" Captions: {len(captions):,}\")\n", "\n", " embeds = {}\n", " for model_name, short, max_len in EXPERTS:\n", " print(f\"\\n Extracting: {short}...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " all_emb = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, len(captions), 128), desc=f\" {short}\"):\n", " batch = captions[i:i+128]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", " embeds[short] = torch.cat(all_emb)\n", " print(f\" Shape: {embeds[short].shape}\")\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 0b: Align + Consensus + Measure ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0b: GENERALIZED PROCRUSTES ALIGNMENT (no reference bias)\")\n", " print(f\"{'='*65}\")\n", "\n", " names = [s for _, s, _ in EXPERTS]\n", "\n", " # Generalized Procrustes: iteratively align all to their mean\n", " # No expert is the reference. The centerpoint emerges.\n", " GPA_ITERS = 10\n", " current = {name: embeds[name].float() for name in names}\n", "\n", " for gpa_iter in range(GPA_ITERS):\n", " # Compute mean shape\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", "\n", " # Align each to mean\n", " new_current = {}\n", " total_delta = 0.0\n", " for name in names:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " # Measure how much this iteration changed things\n", " delta = (new_current[name] - current[name]).pow(2).mean().item()\n", " total_delta += delta\n", "\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter + 1) % 3 == 0 or total_delta < 1e-8:\n", " print(f\" GPA iter {gpa_iter+1}: delta={total_delta:.8f}\")\n", " if total_delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\")\n", " break\n", "\n", " # Final alignment: align each expert to the converged mean\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", " procrustes_results = {}\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name], mean_shape)\n", " procrustes_results[name] = info\n", " aligned[name] = apply_align(embeds[name], info)\n", " cos = F.cosine_similarity(\n", " aligned[name][:2000], mean_shape[:2000], dim=-1).mean().item()\n", " print(f\" {name:10s}: cos_after={info['cos_after']:.4f} cos_to_mean={cos:.4f}\")\n", "\n", " # Consensus = normalized centroid (now equidistant from all experts)\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " for name in names:\n", " cos = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", " # Verify equidistance\n", " expert_cos_to_consensus = []\n", " for name in names:\n", " c = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " expert_cos_to_consensus.append(c)\n", " equidist_range = max(expert_cos_to_consensus) - min(expert_cos_to_consensus)\n", " print(f\" Equidistance range: {equidist_range:.4f} (should be near 0)\")\n", "\n", " # Measure EXACT consensus statistics\n", " print(f\"\\n Measuring consensus statistics...\")\n", " consensus_stats = measure_consensus_stats(consensus)\n", " print(f\" CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Mean cos: {consensus_stats['mean_cos']:.4f}\")\n", " print(f\" Eff dim: {consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Spectral: [{', '.join(f'{s:.4f}' for s in consensus_stats['spectral'][:5])}...]\")\n", "\n", " del embeds, aligned\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 1: Train Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: TRAIN STUDENT\")\n", " print(f\"{'='*65}\")\n", "\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " tokens = tokenizer(captions, max_length=MAX_LEN, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = tokens[\"input_ids\"]\n", " attention_mask = tokens[\"attention_mask\"]\n", "\n", " n_train = N_SAMPLES - 2000\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " student = MiniStudent(\n", " vocab_size=tokenizer.vocab_size, max_len=MAX_LEN,\n", " d_model=256, n_heads=4, n_layers=4, d_ff=1024,\n", " output_dim=768, dropout=0.1, pad_token_id=tokenizer.pad_token_id\n", " ).to(DEVICE)\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Student: {n_params:,} params\")\n", " print(f\" CV target: {consensus_stats['cv']:.4f}\")\n", "\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=3e-4, weight_decay=0.01)\n", "\n", " for epoch in range(5):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " t_loss, t_acc, t_cos, n = 0, 0, 0, 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=consensus_stats[\"cv\"])\n", " loss = l_nce + l_mse + 0.1 * l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", " t_loss += loss.item(); t_acc += acc; t_cos += cos; n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", " student.eval()\n", " with torch.no_grad():\n", " v_emb = student(val_ids, val_mask)\n", " _, v_acc = infonce(v_emb[:1000], val_targets[:1000])\n", " v_cos = F.cosine_similarity(v_emb, val_targets, dim=-1).mean().item()\n", " v_cv = cv_metric(v_emb[:1000])\n", " print(f\" E{epoch+1}: {elapsed:.0f}s loss={t_loss/d:.4f} \"\n", " f\"t_acc={t_acc/d:.3f} t_cos={t_cos/d:.3f} \"\n", " f\"v_acc={v_acc:.3f} v_cos={v_cos:.3f} v_cv={v_cv:.3f}\")\n", "\n", " torch.save(student.state_dict(), \"mini_student.pt\")\n", " print(f\"\\n Student saved. v_cos={v_cos:.3f}, v_cv={v_cv:.3f}\")\n", "\n", " # ── Phase 2: Train Alignment Bank ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: TRAIN ALIGNMENT BANK (student frozen)\")\n", " print(f\"{'='*65}\")\n", "\n", " student.eval()\n", " for p in student.parameters():\n", " p.requires_grad = False\n", "\n", " print(\" Pre-encoding through frozen student...\")\n", " with torch.no_grad():\n", " all_embs = []\n", " for i in range(0, n_train, 512):\n", " j = min(i + 512, n_train)\n", " emb = student(train_ids[i:j], train_mask[i:j])\n", " all_embs.append(emb)\n", " student_embs = torch.cat(all_embs)\n", " val_student_embs = student(val_ids, val_mask)\n", " print(f\" Student embeddings: {student_embs.shape}\")\n", "\n", " bank = AlignmentBank(\n", " d_embed=768, n_experts=len(EXPERTS),\n", " n_anchors=512, d_bank=128\n", " ).to(DEVICE)\n", "\n", " bank.init_from_procrustes(procrustes_results, names,\n", " consensus[:n_train], consensus_stats)\n", " bank_params = sum(p.numel() for p in bank.parameters())\n", " print(f\" Bank: {bank_params:,} params\")\n", " print(f\" Bank targets: CV={bank.target_cv.item():.4f}, \"\n", " f\"mean_cos={bank.target_mean_cos.item():.4f}\")\n", "\n", " # Calibrate disagreement from initial state (before any training)\n", " bank.calibrate_disagreement(student_embs[:2000])\n", "\n", " bank_opt = torch.optim.AdamW(bank.parameters(), lr=1e-3, weight_decay=0.01)\n", " BANK_EPOCHS = 20\n", " BANK_BATCH = 256\n", "\n", " for epoch in range(BANK_EPOCHS):\n", " bank.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " stats = {\"expert_agreement\": 0, \"rotation_ortho\": 0,\n", " \"anchor_spread\": 0, \"bank_cv\": 0, \"emb_cv\": 0,\n", " \"cross_expert_var\": 0, \"disagree_preserve\": 0}\n", " n = 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BANK_BATCH):\n", " idx = perm[i:i+BANK_BATCH]\n", " if len(idx) < 16: continue\n", " emb = student_embs[idx]\n", " enriched, aux = bank(emb)\n", " loss = bank.bank_loss(aux)\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(bank.parameters(), 1.0)\n", " bank_opt.step(); bank_opt.zero_grad(set_to_none=True)\n", " total_loss += loss.item()\n", " for k in stats:\n", " if k in aux:\n", " v = aux[k]\n", " stats[k] += v.item() if torch.is_tensor(v) else v\n", " n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " v_enriched, v_aux = bank(val_student_embs)\n", " v_loss = bank.bank_loss(v_aux).item()\n", "\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} v_loss={v_loss:.4f}\")\n", " print(f\" Geometry: b_cv={stats['bank_cv']/d:.4f} e_cv={stats['emb_cv']/d:.4f} \"\n", " f\"spread={stats['anchor_spread']/d:.5f} a_max={v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Experts: cos={v_aux['expert_cos_mean']:.3f}±{v_aux['expert_cos_std']:.3f} \"\n", " f\"agr={stats['expert_agreement']/d:.6f} ortho={stats['rotation_ortho']/d:.6f}\")\n", " print(f\" Disagree: x_cos={v_aux.get('cross_expert_cos', 0):.4f}±{v_aux.get('cross_expert_cos_std', 0):.4f} \"\n", " f\"ratio={v_aux['disagreement_ratio']:.6f} \"\n", " f\"preserve={stats['disagree_preserve']/d:.6f} \"\n", " f\"norms={v_aux['norm_ratio_spread']:.4f}\")\n", "\n", " torch.save(bank.state_dict(), \"alignment_bank.pt\")\n", "\n", " # ── Phase 3: Geometric Verification ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: GEOMETRIC VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " enriched_val, v_aux = bank(val_student_embs)\n", " original_768 = enriched_val[:, :768]\n", " geo_context = enriched_val[:, 768:]\n", "\n", " passthrough_cos = F.cosine_similarity(\n", " original_768[:100], val_student_embs[:100], dim=-1).mean().item()\n", " geo_cv = cv_metric(F.normalize(geo_context[:1000], dim=-1))\n", " S = torch.linalg.svdvals(\n", " geo_context[:1000].float() - geo_context[:1000].float().mean(0))\n", " geo_eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " # Verify consensus stats are preserved\n", " emb_cv = cv_metric(val_student_embs[:1000])\n", "\n", " print(f\" Passthrough: {passthrough_cos:.6f} (target: 1.000)\")\n", " print(f\" Emb CV: {emb_cv:.4f} (consensus: {consensus_stats['cv']:.4f})\")\n", " print(f\" Geo context CV: {geo_cv:.4f}\")\n", " print(f\" Geo eff_dim: {geo_eff_dim:.1f} / {bank.d_bank}\")\n", " print(f\" Expert cos: {v_aux['expert_cos_mean']:.3f} ± {v_aux['expert_cos_std']:.3f}\")\n", " print(f\" Anchor max cos: {v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Disagreement:\")\n", " print(f\" Cross-expert: {v_aux.get('cross_expert_cos', 0):.4f} ± {v_aux.get('cross_expert_cos_std', 0):.4f}\")\n", " print(f\" Ratio: {v_aux['disagreement_ratio']:.6f} (target: {bank.target_disagreement_ratio.item():.6f})\")\n", " print(f\" Norm spread: {v_aux['norm_ratio_spread']:.4f}\")\n", "\n", " # ── Phase 4: Classifier Stability Test ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: CLASSIFIER STABILITY TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " with torch.no_grad():\n", " embs = val_student_embs[:1000]\n", " sim = embs @ embs.T\n", " sim.fill_diagonal_(-1)\n", " n_pairs = 3000\n", " idx_a = torch.randint(0, 1000, (n_pairs,))\n", " idx_b = torch.randint(0, 1000, (n_pairs,))\n", " pair_cos = sim[idx_a, idx_b]\n", " sorted_cos, _ = pair_cos.sort()\n", " t1 = sorted_cos[n_pairs // 3].item()\n", " t2 = sorted_cos[2 * n_pairs // 3].item()\n", " labels = torch.zeros(n_pairs, dtype=torch.long, device=DEVICE)\n", " labels[pair_cos > t2] = 0\n", " labels[(pair_cos <= t2) & (pair_cos > t1)] = 1\n", " labels[pair_cos <= t1] = 2\n", " enriched_a, _ = bank(embs[idx_a])\n", " enriched_b, _ = bank(embs[idx_b])\n", "\n", " for mode in [\"with_bank\", \"without_bank\"]:\n", " if mode == \"with_bank\":\n", " feat_dim = (768 + 128) * 2\n", " features = torch.cat([enriched_a, enriched_b], dim=-1)\n", " else:\n", " feat_dim = 768 * 2\n", " features = torch.cat([embs[idx_a], embs[idx_b]], dim=-1)\n", "\n", " clf = nn.Sequential(\n", " nn.Linear(feat_dim, 256), nn.GELU(), nn.LayerNorm(256),\n", " nn.Linear(256, 3)\n", " ).to(DEVICE)\n", " clf_opt = torch.optim.Adam(clf.parameters(), lr=1e-3)\n", " n_clf_train = 2400\n", " train_f = features[:n_clf_train].detach()\n", " train_l = labels[:n_clf_train]\n", " val_f = features[n_clf_train:].detach()\n", " val_l = labels[n_clf_train:]\n", " for e in range(30):\n", " clf.train()\n", " logits = clf(train_f)\n", " loss = F.cross_entropy(logits, train_l)\n", " loss.backward(); clf_opt.step(); clf_opt.zero_grad()\n", " clf.eval()\n", " with torch.no_grad():\n", " v_acc = (clf(val_f).argmax(-1) == val_l).float().mean().item()\n", " t_acc = (clf(train_f).argmax(-1) == train_l).float().mean().item()\n", " print(f\" {mode:15s}: train={t_acc:.3f} val={v_acc:.3f} gap={t_acc-v_acc:.3f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Consensus CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Consensus eff_dim:{consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Student v_cos: {v_cos:.3f}\")\n", " print(f\" Student v_cv: {v_cv:.3f}\")\n", " print(f\" Bank params: {bank_params:,}\")\n", " print(f\" Bank geo_eff_dim: {geo_eff_dim:.1f}\")\n", " print(f\" Bank geo_cv: {geo_cv:.4f}\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "c3c92d93759d4f1bacf7b0c9e82fea8c", "d81ed69dac29481098abe7e3367dd946", "eae680c132324913a1bd220bc99343b1", "a3ae20ce4d5d4a8cb5aaa2792f4ac8fb", "d969fd9386254c979c9195b7ba4fc9da", "556728e1ff704dcd8b8110d445984172", "7f0c93c28b2c4b529e15a5baa045c9f2", "7c5142b27e8042a1861a87fb880a3ffd", "1f1255df608f4d3eaec9a2d618a73c2e", "d4cc4e02fcf4457d9c7641457c33851e", "9ba7c36a314741cf837f9fa47e3c4d4e", "9ee9cc7e622641d19fc3d3933f01f69d", "c74ba5f763d94eb8803dfbf92ffe42b7", "ba31d5f5e2614febb1d312c240f7ff3a", "c0391a88ed0940b48f567e11db716d61", "937863ac9ff4471797515158207b6ef6", "e3dcb07e408d441f926c7778dc0a3bba", "b11dbe41f79e4e178ef2e5bc75271414", "43c9d5efd89a4a5ea4ffeb70c55ed556", "a15366153a1a4dd1a034173435d3099e", "23600aa3bd934c5abe36d4ea031ae839", "51e4afb3f3cc4698ada6e1ae1de6bae9" ] }, "id": "wVSD90Ad7GkK", "outputId": "63dee4d2-7c1c-42a1-8cf1-c258f1df543e" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "RAPID PROTOTYPE v2: Differentiation-Centered Bank\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "PHASE 0: EXTRACTION\n", "=================================================================\n", " Captions: 20,000\n", "\n", " Extracting: bert...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 0:\n", " aux[\"cross_expert_var\"] = cross_features.var(dim=0).mean()\n", " else:\n", " aux[\"cross_expert_var\"] = torch.tensor(0.0, device=emb.device)\n", "\n", " # 6. Disagreement preservation\n", " # The distribution of disagreement should stay at the measured target\n", " batch_cross_mean = cross_features.mean() if cross_features.shape[1] > 0 else torch.tensor(0.0, device=emb.device)\n", " batch_cross_std = cross_features.std() if cross_features.shape[1] > 0 else torch.tensor(0.0, device=emb.device)\n", " batch_disagree_ratio = disagreement_ratio.mean()\n", " aux[\"disagree_preserve\"] = (\n", " (batch_cross_mean - self.target_cross_cos_mean).pow(2) +\n", " (batch_cross_std - self.target_cross_cos_std).pow(2) +\n", " (batch_disagree_ratio - self.target_disagreement_ratio).pow(2)\n", " )\n", "\n", " # 7. Bank CV\n", " if B >= 10:\n", " ctx_n = F.normalize(geo_context, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = ctx_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " bank_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"bank_cv\"] = bank_cv\n", " else:\n", " aux[\"bank_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # 8. Emb CV\n", " if B >= 10:\n", " emb_n = F.normalize(emb, dim=-1)\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=embedding.device)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " emb_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " aux[\"emb_cv\"] = emb_cv\n", " else:\n", " aux[\"emb_cv\"] = torch.tensor(0.0, device=embedding.device)\n", "\n", " # Diagnostics\n", " aux[\"expert_cos_mean\"] = expert_cos.mean().item()\n", " aux[\"expert_cos_std\"] = expert_cos.std().item()\n", " aux[\"anchor_max_cos\"] = anchor_cos.max(dim=-1).values.mean().item()\n", " aux[\"anchor_mean_cos\"] = anchor_cos.mean().item()\n", " if cross_features.shape[1] > 0:\n", " aux[\"cross_expert_cos\"] = cross_features.mean().item()\n", " aux[\"cross_expert_cos_std\"] = cross_features.std().item()\n", " aux[\"disagreement_ratio\"] = disagreement_ratio.mean().item()\n", " aux[\"norm_ratio_spread\"] = norm_ratio.std(dim=-1).mean().item()\n", "\n", " return enriched, aux\n", "\n", " def bank_loss(self, aux):\n", " \"\"\"All targets from measured consensus. Preserves disagreement structure.\"\"\"\n", " loss = (\n", " 1.0 * aux[\"expert_agreement\"] +\n", " 1.0 * aux[\"rotation_ortho\"] +\n", " 0.5 * aux[\"anchor_spread\"] +\n", " 0.1 * aux[\"anchor_entropy\"] +\n", " 0.3 * aux[\"cross_expert_var\"] +\n", " 0.3 * (aux[\"bank_cv\"] - self.target_cv).abs() +\n", " 0.3 * (aux[\"emb_cv\"] - self.target_cv).abs() +\n", " 0.5 * aux[\"disagree_preserve\"] # preserve the disagreement distribution\n", " )\n", " return loss\n", "\n", " @torch.no_grad()\n", " def calibrate_disagreement(self, embeddings):\n", " \"\"\"\n", " Measure the initial disagreement structure from per-sample distribution.\n", " Uses the full batch to capture the spread, not just the mean.\n", " \"\"\"\n", " B = embeddings.shape[0]\n", " emb = embeddings.float()\n", "\n", " # Compute per-sample disagreement directly\n", " per_sample_expert_cos = []\n", " for i in range(self.n_experts):\n", " R = self.expert_rotations[i]\n", " W = self.expert_whiteners[i]\n", " mu = self.expert_means[i]\n", " centered = emb - mu\n", " whitened = centered @ W\n", " whitened_n = F.normalize(whitened, dim=-1)\n", " in_expert = whitened_n @ R.T\n", " back = in_expert @ R\n", " cos = F.cosine_similarity(whitened_n, back, dim=-1)\n", " per_sample_expert_cos.append(cos)\n", "\n", " expert_cos = torch.stack(per_sample_expert_cos, dim=-1) # (B, n_experts)\n", " per_sample_agreement = expert_cos.mean(dim=-1)\n", " per_sample_disagreement = expert_cos.std(dim=-1)\n", " per_sample_ratio = per_sample_disagreement / (per_sample_agreement + 1e-8)\n", "\n", " # Cross-expert cosines\n", " cross_vals = []\n", " expert_projected = []\n", " for i in range(self.n_experts):\n", " R = self.expert_rotations[i]\n", " W = self.expert_whiteners[i]\n", " mu = self.expert_means[i]\n", " centered = emb - mu\n", " whitened = centered @ W\n", " whitened_n = F.normalize(whitened, dim=-1)\n", " expert_projected.append(whitened_n @ R.T)\n", "\n", " for i in range(self.n_experts):\n", " for j in range(i + 1, self.n_experts):\n", " cc = F.cosine_similarity(expert_projected[i], expert_projected[j], dim=-1)\n", " cross_vals.append(cc)\n", "\n", " if cross_vals:\n", " cross_all = torch.stack(cross_vals, dim=-1)\n", " self.target_cross_cos_mean.fill_(cross_all.mean().item())\n", " self.target_cross_cos_std.fill_(cross_all.std().item())\n", "\n", " # Use MEDIAN of per-sample ratio (robust to outliers)\n", " self.target_disagreement_ratio.fill_(per_sample_ratio.median().item())\n", "\n", " print(f\" Calibrated disagreement (n={B}):\")\n", " print(f\" cross_cos: {self.target_cross_cos_mean.item():.4f} ± {self.target_cross_cos_std.item():.4f}\")\n", " print(f\" disagree_ratio: median={self.target_disagreement_ratio.item():.6f} \"\n", " f\"mean={per_sample_ratio.mean().item():.6f} \"\n", " f\"std={per_sample_ratio.std().item():.6f}\")\n", " print(f\" expert_cos: {expert_cos.mean().item():.4f} ± {expert_cos.std().item():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.12, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def measure_consensus_stats(consensus_embs, n_check=2000):\n", " \"\"\"Measure exact geometric statistics of the consensus manifold.\"\"\"\n", " embs = consensus_embs[:n_check].float()\n", " # CV\n", " cv = cv_metric(embs.to(DEVICE))\n", " # Pairwise cosine\n", " sim = embs @ embs.T\n", " mask = ~torch.eye(embs.shape[0], dtype=torch.bool)\n", " pairwise = sim[mask]\n", " mean_cos = pairwise.mean().item()\n", " # Spectral\n", " centered = embs - embs.mean(0, keepdim=True)\n", " S = torch.linalg.svdvals(centered)\n", " S_norm = (S / (S.sum() + 1e-8)).tolist()[:50]\n", " # Eff dim\n", " eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " return {\n", " \"cv\": cv,\n", " \"mean_cos\": mean_cos,\n", " \"spectral\": S_norm,\n", " \"eff_dim\": eff_dim,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACTION + ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " N_SAMPLES = 20000\n", " MAX_LEN = 128\n", " BATCH = 256\n", "\n", " # ── Phase 0: Extract ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: EXTRACTION\")\n", " print(f\"{'='*65}\")\n", "\n", " from datasets import load_dataset\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_SAMPLES:\n", " break\n", " print(f\" Captions: {len(captions):,}\")\n", "\n", " embeds = {}\n", " for model_name, short, max_len in EXPERTS:\n", " print(f\"\\n Extracting: {short}...\")\n", " model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " all_emb = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, len(captions), 128), desc=f\" {short}\"):\n", " batch = captions[i:i+128]\n", " inputs = tokenizer(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", " embeds[short] = torch.cat(all_emb)\n", " print(f\" Shape: {embeds[short].shape}\")\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 0b: Align + Consensus + Measure ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0b: GENERALIZED PROCRUSTES ALIGNMENT (no reference bias)\")\n", " print(f\"{'='*65}\")\n", "\n", " names = [s for _, s, _ in EXPERTS]\n", "\n", " # Generalized Procrustes: iteratively align all to their mean\n", " # No expert is the reference. The centerpoint emerges.\n", " GPA_ITERS = 10\n", " current = {name: embeds[name].float() for name in names}\n", "\n", " for gpa_iter in range(GPA_ITERS):\n", " # Compute mean shape\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", "\n", " # Align each to mean\n", " new_current = {}\n", " total_delta = 0.0\n", " for name in names:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " # Measure how much this iteration changed things\n", " delta = (new_current[name] - current[name]).pow(2).mean().item()\n", " total_delta += delta\n", "\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter + 1) % 3 == 0 or total_delta < 1e-8:\n", " print(f\" GPA iter {gpa_iter+1}: delta={total_delta:.8f}\")\n", " if total_delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\")\n", " break\n", "\n", " # Final alignment: align each expert to the converged mean\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", " procrustes_results = {}\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name], mean_shape)\n", " procrustes_results[name] = info\n", " aligned[name] = apply_align(embeds[name], info)\n", " cos = F.cosine_similarity(\n", " aligned[name][:2000], mean_shape[:2000], dim=-1).mean().item()\n", " print(f\" {name:10s}: cos_after={info['cos_after']:.4f} cos_to_mean={cos:.4f}\")\n", "\n", " # Consensus = normalized centroid (now equidistant from all experts)\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " for name in names:\n", " cos = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", " # Verify equidistance\n", " expert_cos_to_consensus = []\n", " for name in names:\n", " c = F.cosine_similarity(consensus[:2000], aligned[name][:2000], dim=-1).mean().item()\n", " expert_cos_to_consensus.append(c)\n", " equidist_range = max(expert_cos_to_consensus) - min(expert_cos_to_consensus)\n", " print(f\" Equidistance range: {equidist_range:.4f} (should be near 0)\")\n", "\n", " # Measure EXACT consensus statistics\n", " print(f\"\\n Measuring consensus statistics...\")\n", " consensus_stats = measure_consensus_stats(consensus)\n", " print(f\" CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Mean cos: {consensus_stats['mean_cos']:.4f}\")\n", " print(f\" Eff dim: {consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Spectral: [{', '.join(f'{s:.4f}' for s in consensus_stats['spectral'][:5])}...]\")\n", "\n", " del embeds, aligned\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 1: Train Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: TRAIN STUDENT\")\n", " print(f\"{'='*65}\")\n", "\n", " tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " tokens = tokenizer(captions, max_length=MAX_LEN, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = tokens[\"input_ids\"]\n", " attention_mask = tokens[\"attention_mask\"]\n", "\n", " n_train = N_SAMPLES - 2000\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:].to(DEVICE)\n", " val_mask = attention_mask[n_train:].to(DEVICE)\n", " val_targets = consensus[n_train:].to(DEVICE)\n", "\n", " student = MiniStudent(\n", " vocab_size=tokenizer.vocab_size, max_len=MAX_LEN,\n", " d_model=256, n_heads=8, n_layers=8, d_ff=1024,\n", " output_dim=768, dropout=0.1, pad_token_id=tokenizer.pad_token_id\n", " ).to(DEVICE)\n", " n_params = sum(p.numel() for p in student.parameters())\n", " print(f\" Student: {n_params:,} params\")\n", " print(f\" CV target: {consensus_stats['cv']:.4f}\")\n", "\n", " optimizer = torch.optim.AdamW(student.parameters(), lr=3e-4, weight_decay=0.01)\n", "\n", " for epoch in range(10):\n", " student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " t_loss, t_acc, t_cos, n = 0, 0, 0, 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", " emb = student(train_ids[idx], train_mask[idx])\n", " tgt = train_targets[idx]\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=consensus_stats[\"cv\"])\n", " loss = l_nce + l_mse + 0.1 * l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", " t_loss += loss.item(); t_acc += acc; t_cos += cos; n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", " student.eval()\n", " with torch.no_grad():\n", " v_emb = student(val_ids, val_mask)\n", " _, v_acc = infonce(v_emb[:1000], val_targets[:1000])\n", " v_cos = F.cosine_similarity(v_emb, val_targets, dim=-1).mean().item()\n", " v_cv = cv_metric(v_emb[:1000])\n", " print(f\" E{epoch+1}: {elapsed:.0f}s loss={t_loss/d:.4f} \"\n", " f\"t_acc={t_acc/d:.3f} t_cos={t_cos/d:.3f} \"\n", " f\"v_acc={v_acc:.3f} v_cos={v_cos:.3f} v_cv={v_cv:.3f}\")\n", "\n", " torch.save(student.state_dict(), \"mini_student.pt\")\n", " print(f\"\\n Student saved. v_cos={v_cos:.3f}, v_cv={v_cv:.3f}\")\n", "\n", " # ── Phase 2: Train Alignment Bank ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: TRAIN ALIGNMENT BANK (student frozen)\")\n", " print(f\"{'='*65}\")\n", "\n", " student.eval()\n", " for p in student.parameters():\n", " p.requires_grad = False\n", "\n", " print(\" Pre-encoding through frozen student...\")\n", " with torch.no_grad():\n", " all_embs = []\n", " for i in range(0, n_train, 512):\n", " j = min(i + 512, n_train)\n", " emb = student(train_ids[i:j], train_mask[i:j])\n", " all_embs.append(emb)\n", " student_embs = torch.cat(all_embs)\n", " val_student_embs = student(val_ids, val_mask)\n", " print(f\" Student embeddings: {student_embs.shape}\")\n", "\n", " bank = AlignmentBank(\n", " d_embed=768, n_experts=len(EXPERTS),\n", " n_anchors=512, d_bank=128\n", " ).to(DEVICE)\n", "\n", " bank.init_from_procrustes(procrustes_results, names,\n", " consensus[:n_train], consensus_stats)\n", " bank_params = sum(p.numel() for p in bank.parameters())\n", " print(f\" Bank: {bank_params:,} params\")\n", " print(f\" Bank targets: CV={bank.target_cv.item():.4f}, \"\n", " f\"mean_cos={bank.target_mean_cos.item():.4f}\")\n", "\n", " # Calibrate disagreement from initial state (before any training)\n", " bank.calibrate_disagreement(student_embs[:2000])\n", "\n", " bank_opt = torch.optim.AdamW(bank.parameters(), lr=1e-3, weight_decay=0.01)\n", " BANK_EPOCHS = 20\n", " BANK_BATCH = 256\n", "\n", " for epoch in range(BANK_EPOCHS):\n", " bank.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " stats = {\"expert_agreement\": 0, \"rotation_ortho\": 0,\n", " \"anchor_spread\": 0, \"bank_cv\": 0, \"emb_cv\": 0,\n", " \"cross_expert_var\": 0, \"disagree_preserve\": 0}\n", " n = 0\n", " t0 = time.time()\n", " for i in range(0, n_train, BANK_BATCH):\n", " idx = perm[i:i+BANK_BATCH]\n", " if len(idx) < 16: continue\n", " emb = student_embs[idx]\n", " enriched, aux = bank(emb)\n", " loss = bank.bank_loss(aux)\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(bank.parameters(), 1.0)\n", " bank_opt.step(); bank_opt.zero_grad(set_to_none=True)\n", " total_loss += loss.item()\n", " for k in stats:\n", " if k in aux:\n", " v = aux[k]\n", " stats[k] += v.item() if torch.is_tensor(v) else v\n", " n += 1\n", " elapsed = time.time() - t0; d = max(n, 1)\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " v_enriched, v_aux = bank(val_student_embs)\n", " v_loss = bank.bank_loss(v_aux).item()\n", "\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} v_loss={v_loss:.4f}\")\n", " print(f\" Geometry: b_cv={stats['bank_cv']/d:.4f} e_cv={stats['emb_cv']/d:.4f} \"\n", " f\"spread={stats['anchor_spread']/d:.5f} a_max={v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Experts: cos={v_aux['expert_cos_mean']:.3f}±{v_aux['expert_cos_std']:.3f} \"\n", " f\"agr={stats['expert_agreement']/d:.6f} ortho={stats['rotation_ortho']/d:.6f}\")\n", " print(f\" Disagree: x_cos={v_aux.get('cross_expert_cos', 0):.4f}±{v_aux.get('cross_expert_cos_std', 0):.4f} \"\n", " f\"ratio={v_aux['disagreement_ratio']:.6f} \"\n", " f\"preserve={stats['disagree_preserve']/d:.6f} \"\n", " f\"norms={v_aux['norm_ratio_spread']:.4f}\")\n", "\n", " torch.save(bank.state_dict(), \"alignment_bank.pt\")\n", "\n", " # ── Phase 3: Geometric Verification ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: GEOMETRIC VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " enriched_val, v_aux = bank(val_student_embs)\n", " original_768 = enriched_val[:, :768]\n", " geo_context = enriched_val[:, 768:]\n", "\n", " passthrough_cos = F.cosine_similarity(\n", " original_768[:100], val_student_embs[:100], dim=-1).mean().item()\n", " geo_cv = cv_metric(F.normalize(geo_context[:1000], dim=-1))\n", " S = torch.linalg.svdvals(\n", " geo_context[:1000].float() - geo_context[:1000].float().mean(0))\n", " geo_eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", "\n", " # Verify consensus stats are preserved\n", " emb_cv = cv_metric(val_student_embs[:1000])\n", "\n", " print(f\" Passthrough: {passthrough_cos:.6f} (target: 1.000)\")\n", " print(f\" Emb CV: {emb_cv:.4f} (consensus: {consensus_stats['cv']:.4f})\")\n", " print(f\" Geo context CV: {geo_cv:.4f}\")\n", " print(f\" Geo eff_dim: {geo_eff_dim:.1f} / {bank.d_bank}\")\n", " print(f\" Expert cos: {v_aux['expert_cos_mean']:.3f} ± {v_aux['expert_cos_std']:.3f}\")\n", " print(f\" Anchor max cos: {v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Disagreement:\")\n", " print(f\" Cross-expert: {v_aux.get('cross_expert_cos', 0):.4f} ± {v_aux.get('cross_expert_cos_std', 0):.4f}\")\n", " print(f\" Ratio: {v_aux['disagreement_ratio']:.6f} (target: {bank.target_disagreement_ratio.item():.6f})\")\n", " print(f\" Norm spread: {v_aux['norm_ratio_spread']:.4f}\")\n", "\n", " # ── Phase 4: Classifier Stability Test ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: CLASSIFIER STABILITY TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " with torch.no_grad():\n", " embs = val_student_embs[:1000]\n", " sim = embs @ embs.T\n", " sim.fill_diagonal_(-1)\n", " n_pairs = 3000\n", " idx_a = torch.randint(0, 1000, (n_pairs,))\n", " idx_b = torch.randint(0, 1000, (n_pairs,))\n", " pair_cos = sim[idx_a, idx_b]\n", " sorted_cos, _ = pair_cos.sort()\n", " t1 = sorted_cos[n_pairs // 3].item()\n", " t2 = sorted_cos[2 * n_pairs // 3].item()\n", " labels = torch.zeros(n_pairs, dtype=torch.long, device=DEVICE)\n", " labels[pair_cos > t2] = 0\n", " labels[(pair_cos <= t2) & (pair_cos > t1)] = 1\n", " labels[pair_cos <= t1] = 2\n", "\n", " enriched_a, aux_a = bank(embs[idx_a])\n", " enriched_b, aux_b = bank(embs[idx_b])\n", "\n", " # Build explicit geometric features per pair\n", " # These are interpretable and hard to overfit\n", " a_emb = embs[idx_a]; b_emb = embs[idx_b]\n", " a_geo = enriched_a[:, 768:]; b_geo = enriched_b[:, 768:]\n", "\n", " geo_explicit = torch.cat([\n", " # Pair-level\n", " F.cosine_similarity(a_emb, b_emb, dim=-1).unsqueeze(-1), # raw cosine\n", " (a_emb - b_emb).pow(2).mean(dim=-1).unsqueeze(-1), # MSE\n", " F.cosine_similarity(a_geo, b_geo, dim=-1).unsqueeze(-1), # geo context cosine\n", " (a_geo - b_geo).pow(2).mean(dim=-1).unsqueeze(-1), # geo context MSE\n", " # Per-sample bank diagnostics (already computed in forward)\n", " torch.abs(a_emb - b_emb).mean(dim=-1).unsqueeze(-1), # L1 distance\n", " (a_emb * b_emb).sum(dim=-1).unsqueeze(-1), # dot product\n", " ], dim=-1) # (n_pairs, 6)\n", "\n", " modes = {\n", " \"raw_768\": torch.cat([a_emb, b_emb], dim=-1),\n", " \"raw+diff\": torch.cat([a_emb, b_emb, torch.abs(a_emb - b_emb), a_emb * b_emb], dim=-1),\n", " \"bank_enriched\": torch.cat([enriched_a, enriched_b], dim=-1),\n", " \"bank+diff\": torch.cat([enriched_a, enriched_b,\n", " torch.abs(enriched_a - enriched_b),\n", " enriched_a * enriched_b], dim=-1),\n", " \"geo_explicit\": geo_explicit,\n", " }\n", "\n", " print(f\"\\n {'Mode':<20} {'Dim':>6} {'Train':>7} {'Val':>7} {'Gap':>7}\")\n", " print(f\" {'-'*50}\")\n", "\n", " for mode_name, features in modes.items():\n", " feat_dim = features.shape[1]\n", " clf = nn.Sequential(\n", " nn.Linear(feat_dim, min(256, feat_dim)), nn.GELU(), nn.LayerNorm(min(256, feat_dim)),\n", " nn.Dropout(0.1),\n", " nn.Linear(min(256, feat_dim), 3)\n", " ).to(DEVICE)\n", " clf_opt = torch.optim.Adam(clf.parameters(), lr=1e-3)\n", " n_clf_train = 2400\n", " train_f = features[:n_clf_train].detach()\n", " train_l = labels[:n_clf_train]\n", " val_f = features[n_clf_train:].detach()\n", " val_l = labels[n_clf_train:]\n", " for e in range(30):\n", " clf.train()\n", " logits = clf(train_f)\n", " loss = F.cross_entropy(logits, train_l)\n", " loss.backward(); clf_opt.step(); clf_opt.zero_grad()\n", " clf.eval()\n", " with torch.no_grad():\n", " v_acc = (clf(val_f).argmax(-1) == val_l).float().mean().item()\n", " t_acc = (clf(train_f).argmax(-1) == train_l).float().mean().item()\n", " print(f\" {mode_name:<20} {feat_dim:>6} {t_acc:>7.3f} {v_acc:>7.3f} {t_acc-v_acc:>7.3f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Consensus CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Consensus eff_dim:{consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Student v_cos: {v_cos:.3f}\")\n", " print(f\" Student v_cv: {v_cv:.3f}\")\n", " print(f\" Bank params: {bank_params:,}\")\n", " print(f\" Bank geo_eff_dim: {geo_eff_dim:.1f}\")\n", " print(f\" Bank geo_cv: {geo_cv:.4f}\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "adf5073645bd4927b23e9e2f245e57bc", "41fa9a998197421db2e37852bd643d20", "3c28a35ed90345d5983b6e7910e0076e", "19460177f35546d7af56905bfa79dbe9", "609d8ace2dca4f9aa239ece209512e3c", "4d1db66017314a47917b4f7e370b2da3", "78512c07da324a4d91baff786f68f79b", "e6e0a63481964fbe982e006f2d855bbb", "ebcea318e84145b6a5d238df793755da", "a706ddc178fa405a9a058283431ae4a7", "536780f5538e4c148d6c08014a76f670", "8acc67491474462e9e062baca52c04a2", "2eb6770aee654f388176238f09778b30", "95b5c43c48594913ae0d8e315a9c80f9", "fb6a8aa0a91b4bb0adc448ad84b1428b", "2e96ea3acbad4fee9d528107a1e527af", "aa01aa0a79e1496c82c8a0278cf9c3da", "3440899e994a4b68971ad5924d9db1ad", "e761b132c88240549199f12afaf5a650", "cb3b9be5979a4f9cb8a8186d1e324cdc", "b343f92042b94bc78eb0af35fd65a1bf", "4d73be6b0016448da67fa630570807f2" ] }, "id": "MucNEUth94EL", "outputId": "4e982950-dd69-4ff6-8338-4ee3540c05c4" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "RAPID PROTOTYPE v2: Differentiation-Centered Bank\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "PHASE 0: EXTRACTION\n", "=================================================================\n", " Captions: 20,000\n", "\n", " Extracting: bert...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00= 10:\n", " vols = []\n", " for _ in range(32):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " pts = data[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " aux[label] = stacked.std() / (stacked.mean() + 1e-8)\n", " else:\n", " aux[label] = torch.tensor(0.0, device=emb.device)\n", "\n", " # Diagnostics\n", " aux[\"expert_cos_mean\"] = expert_cos.mean().item()\n", " aux[\"expert_cos_std\"] = expert_cos.std().item()\n", " aux[\"anchor_max_cos\"] = anchor_cos.max(dim=-1).values.mean().item()\n", " aux[\"cross_expert_cos\"] = cross_features.mean().item()\n", " aux[\"cross_expert_cos_std\"] = cross_features.std().item()\n", " aux[\"disagreement_ratio\"] = disagreement_ratio.mean().item()\n", " aux[\"norm_ratio_spread\"] = norm_ratio.std(dim=-1).mean().item()\n", "\n", " return enriched, aux\n", "\n", " def bank_loss(self, aux):\n", " return (\n", " 1.0 * aux[\"expert_agreement\"] +\n", " 1.0 * aux[\"rotation_ortho\"] +\n", " 0.5 * aux[\"anchor_spread\"] +\n", " 0.1 * aux[\"anchor_entropy\"] +\n", " 0.3 * aux[\"cross_expert_var\"] +\n", " 0.3 * (aux[\"bank_cv\"] - self.target_cv).abs() +\n", " 0.3 * (aux[\"emb_cv\"] - self.target_cv).abs() +\n", " 0.5 * aux[\"disagree_preserve\"])\n", "\n", " @torch.no_grad()\n", " def calibrate_disagreement(self, embeddings):\n", " B = embeddings.shape[0]\n", " emb = embeddings.float()\n", " per_sample_expert_cos = []\n", " expert_projected = []\n", " for i in range(self.n_experts):\n", " R = self.expert_rotations[i]; W = self.expert_whiteners[i]; mu = self.expert_means[i]\n", " centered = emb - mu; whitened = centered @ W\n", " whitened_n = F.normalize(whitened, dim=-1)\n", " in_expert = whitened_n @ R.T\n", " back = in_expert @ R\n", " per_sample_expert_cos.append(F.cosine_similarity(whitened_n, back, dim=-1))\n", " expert_projected.append(in_expert)\n", " expert_cos = torch.stack(per_sample_expert_cos, dim=-1)\n", " per_sample_ratio = expert_cos.std(dim=-1) / (expert_cos.mean(dim=-1) + 1e-8)\n", " cross_vals = []\n", " for i in range(self.n_experts):\n", " for j in range(i + 1, self.n_experts):\n", " cross_vals.append(F.cosine_similarity(expert_projected[i], expert_projected[j], dim=-1))\n", " cross_all = torch.stack(cross_vals, dim=-1)\n", " self.target_cross_cos_mean.fill_(cross_all.mean().item())\n", " self.target_cross_cos_std.fill_(cross_all.std().item())\n", " self.target_disagreement_ratio.fill_(per_sample_ratio.median().item())\n", " print(f\" Calibrated (n={B}):\")\n", " print(f\" cross_cos: {self.target_cross_cos_mean.item():.4f} ± {self.target_cross_cos_std.item():.4f}\")\n", " print(f\" disagree_ratio: median={self.target_disagreement_ratio.item():.6f}\")\n", " print(f\" expert_cos: {expert_cos.mean().item():.4f} ± {expert_cos.std().item():.4f}\")\n", " print(f\" cross pairs: {len(cross_vals)}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ALIGNMENT UTILITIES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\n", " \"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after,\n", " }\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " pts = emb[idx].unsqueeze(0).float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " names = [s for _, s, _ in EXPERTS]\n", "\n", " # ── Phase 0: Extract or Load Embeddings ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: EXPERT EMBEDDINGS\")\n", " print(f\"{'='*65}\")\n", "\n", " os.makedirs(CACHE_DIR, exist_ok=True)\n", " caps_path = os.path.join(CACHE_DIR, \"captions.json\")\n", "\n", " # Check what's cached\n", " all_cached = all(\n", " os.path.exists(os.path.join(CACHE_DIR, f\"{s}.pt\"))\n", " for _, s, _ in EXPERTS)\n", "\n", " if all_cached:\n", " print(\" Loading cached embeddings...\")\n", " embeds = {}\n", " for _, short, _ in EXPERTS:\n", " embeds[short] = torch.load(\n", " os.path.join(CACHE_DIR, f\"{short}.pt\"), weights_only=True)\n", " print(f\" {short}: {embeds[short].shape}\")\n", " if os.path.exists(caps_path):\n", " with open(caps_path) as f:\n", " captions = json.load(f)\n", " print(f\" Captions: {len(captions):,}\")\n", " else:\n", " print(\" captions.json missing, reloading...\")\n", " from datasets import load_dataset\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_EXTRACT:\n", " break\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", " else:\n", " # Extract from scratch\n", " from datasets import load_dataset\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " print(f\" Loading {N_EXTRACT:,} captions...\")\n", " ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=True)\n", " captions = []\n", " for row in ds:\n", " cap = row.get(\"caption_llava\", \"\")\n", " if isinstance(cap, str) and len(cap) > 50:\n", " captions.append(cap)\n", " if len(captions) >= N_EXTRACT:\n", " break\n", " print(f\" Got {len(captions):,} captions\")\n", "\n", " with open(caps_path, \"w\") as f:\n", " json.dump(captions, f)\n", "\n", " embeds = {}\n", " for model_name, short, max_len in EXPERTS:\n", " out_path = os.path.join(CACHE_DIR, f\"{short}.pt\")\n", " if os.path.exists(out_path):\n", " embeds[short] = torch.load(out_path, weights_only=True)\n", " print(f\" {short}: cached {embeds[short].shape}\")\n", " continue\n", "\n", " print(f\"\\n Extracting: {short} ({model_name}, max_len={max_len})...\")\n", " ext_model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " ext_tok = AutoTokenizer.from_pretrained(model_name)\n", " n_p = sum(p.numel() for p in ext_model.parameters())\n", " print(f\" {n_p:,} params\")\n", "\n", " all_emb = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, len(captions), 128), desc=f\" {short}\"):\n", " batch = captions[i:i+128]\n", " inputs = ext_tok(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = ext_model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", "\n", " emb = torch.cat(all_emb)\n", " if emb.shape[1] != 768:\n", " emb = emb[:, :768] if emb.shape[1] > 768 else F.pad(emb, (0, 768 - emb.shape[1]))\n", " embeds[short] = emb\n", " torch.save(emb, out_path)\n", " print(f\" Saved: {emb.shape}\")\n", " del ext_model, ext_tok; gc.collect(); torch.cuda.empty_cache()\n", "\n", " N = min(len(captions), min(e.shape[0] for e in embeds.values()))\n", " print(f\" Using {N:,} samples\")\n", "\n", " # ── Phase 1: GPA Alignment ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: GENERALIZED PROCRUSTES ALIGNMENT\")\n", " print(f\"{'='*65}\")\n", "\n", " current = {name: embeds[name][:N].float() for name in names}\n", " for gpa_iter in range(15):\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", " total_delta = 0.0\n", " new_current = {}\n", " for name in names:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " total_delta += (new_current[name] - current[name]).pow(2).mean().item()\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter + 1) % 3 == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={total_delta:.8f}\")\n", " if total_delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\")\n", " break\n", "\n", " # Final alignment to converged mean\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", " procrustes_results = {}\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name][:N], mean_shape)\n", " procrustes_results[name] = info\n", " aligned[name] = apply_align(embeds[name][:N], info)\n", " cos_to_mean = F.cosine_similarity(\n", " aligned[name][:5000], mean_shape[:5000], dim=-1).mean().item()\n", " print(f\" {name:10s}: cos_after={info['cos_after']:.4f} cos_to_mean={cos_to_mean:.4f}\")\n", "\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " expert_cos_to_consensus = []\n", " for name in names:\n", " c = F.cosine_similarity(consensus[:5000], aligned[name][:5000], dim=-1).mean().item()\n", " expert_cos_to_consensus.append(c)\n", " print(f\" cos(consensus, {name}): {c:.4f}\")\n", " equidist = max(expert_cos_to_consensus) - min(expert_cos_to_consensus)\n", " print(f\" Equidistance range: {equidist:.4f}\")\n", "\n", " # Measure consensus statistics\n", " print(f\"\\n Measuring consensus statistics...\")\n", " c_sub = consensus[:5000].to(DEVICE)\n", " consensus_cv = cv_metric(c_sub)\n", " sim = c_sub @ c_sub.T\n", " mask = ~torch.eye(5000, dtype=torch.bool, device=DEVICE)\n", " mean_cos = sim[mask].mean().item()\n", " centered = c_sub.float() - c_sub.float().mean(0, keepdim=True)\n", " S = torch.linalg.svdvals(centered)\n", " spectral = (S / (S.sum() + 1e-8)).cpu().tolist()[:50]\n", " eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", " consensus_stats = {\"cv\": consensus_cv, \"mean_cos\": mean_cos,\n", " \"spectral\": spectral, \"eff_dim\": eff_dim}\n", " print(f\" CV: {consensus_cv:.4f}\")\n", " print(f\" Mean cos: {mean_cos:.4f}\")\n", " print(f\" Eff dim: {eff_dim:.1f}\")\n", " del c_sub, sim; torch.cuda.empty_cache()\n", "\n", " del embeds, aligned, current, mean_shape\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 2: Load + Encode Frozen Student ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: ENCODE FROZEN STUDENT\")\n", " print(f\"{'='*65}\")\n", "\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", " for p in model.parameters():\n", " p.requires_grad = False\n", " print(f\" Student: {sum(p.numel() for p in model.parameters()):,} params (frozen)\")\n", "\n", " # captions already loaded from Phase 0\n", " captions = captions[:N]\n", " print(f\" Encoding {N:,} captions...\")\n", " all_student_embs = []\n", " with torch.no_grad():\n", " for i in tqdm(range(0, N, 256), desc=\" Encoding\"):\n", " j = min(i + 256, N)\n", " inputs = tokenizer(captions[i:j], max_length=512, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", " all_student_embs.append(out.last_hidden_state.cpu())\n", " student_embs = torch.cat(all_student_embs).to(DEVICE)\n", " print(f\" Student embeddings: {student_embs.shape}\")\n", "\n", " del model\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # Split\n", " n_train = N - N_VAL\n", " train_embs = student_embs[:n_train]\n", " val_embs = student_embs[n_train:n_train + N_VAL]\n", " print(f\" Train: {n_train:,} Val: {N_VAL:,}\")\n", "\n", " # ── Phase 3: Train Alignment Bank ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: TRAIN ALIGNMENT BANK\")\n", " print(f\"{'='*65}\")\n", "\n", " bank = AlignmentBank(\n", " d_embed=768, n_experts=len(EXPERTS),\n", " n_anchors=N_ANCHORS, d_bank=D_BANK\n", " ).to(DEVICE)\n", "\n", " bank.init_from_procrustes(procrustes_results, names,\n", " consensus[:n_train], consensus_stats)\n", " bank.calibrate_disagreement(train_embs[:5000])\n", "\n", " bank_params = sum(p.numel() for p in bank.parameters())\n", " print(f\" Bank: {bank_params:,} params\")\n", "\n", " bank_opt = torch.optim.AdamW(bank.parameters(), lr=BANK_LR, weight_decay=0.01)\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " bank_opt, T_max=(n_train // BANK_BATCH) * BANK_EPOCHS, eta_min=1e-5)\n", "\n", " best_v_loss = float(\"inf\")\n", " for epoch in range(BANK_EPOCHS):\n", " bank.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss = 0\n", " stats = {\"expert_agreement\": 0, \"rotation_ortho\": 0, \"anchor_spread\": 0,\n", " \"bank_cv\": 0, \"emb_cv\": 0, \"cross_expert_var\": 0, \"disagree_preserve\": 0}\n", " n = 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, BANK_BATCH):\n", " idx = perm[i:i+BANK_BATCH]\n", " if len(idx) < 16: continue\n", " _, aux = bank(train_embs[idx])\n", " loss = bank.bank_loss(aux)\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(bank.parameters(), 1.0)\n", " bank_opt.step(); bank_opt.zero_grad(set_to_none=True)\n", " scheduler.step()\n", " total_loss += loss.item()\n", " for k in stats:\n", " if k in aux:\n", " v = aux[k]\n", " stats[k] += v.item() if torch.is_tensor(v) else v\n", " n += 1\n", "\n", " elapsed = time.time() - t0; d = max(n, 1)\n", "\n", " bank.eval()\n", " with torch.no_grad():\n", " _, v_aux = bank(val_embs)\n", " v_loss = bank.bank_loss(v_aux).item()\n", "\n", " if v_loss < best_v_loss:\n", " best_v_loss = v_loss\n", " torch.save(bank.state_dict(), \"alignment_bank_best.pt\")\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} v_loss={v_loss:.4f}\")\n", " print(f\" Geometry: b_cv={stats['bank_cv']/d:.4f} e_cv={stats['emb_cv']/d:.4f} \"\n", " f\"spread={stats['anchor_spread']/d:.5f} a_max={v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Experts: cos={v_aux['expert_cos_mean']:.3f}±{v_aux['expert_cos_std']:.3f} \"\n", " f\"agr={stats['expert_agreement']/d:.6f} ortho={stats['rotation_ortho']/d:.6f}\")\n", " print(f\" Disagree: x_cos={v_aux['cross_expert_cos']:.4f}±{v_aux['cross_expert_cos_std']:.4f} \"\n", " f\"ratio={v_aux['disagreement_ratio']:.6f} \"\n", " f\"preserve={stats['disagree_preserve']/d:.6f}\")\n", " else:\n", " print(f\" E{epoch+1:2d}: {elapsed:.0f}s loss={total_loss/d:.4f} v_loss={v_loss:.4f} \"\n", " f\"exp={v_aux['expert_cos_mean']:.3f} \"\n", " f\"b_cv={stats['bank_cv']/d:.4f} \"\n", " f\"x_cos={v_aux['cross_expert_cos']:.4f}\")\n", "\n", " torch.save(bank.state_dict(), \"alignment_bank_final.pt\")\n", "\n", " # ── Phase 4: Geometric Verification ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: GEOMETRIC VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " bank.load_state_dict(torch.load(\"alignment_bank_best.pt\", weights_only=True))\n", " bank.eval()\n", "\n", " with torch.no_grad():\n", " enriched_val, v_aux = bank(val_embs)\n", " original_768 = enriched_val[:, :768]\n", " geo_context = enriched_val[:, 768:]\n", "\n", " passthrough = F.cosine_similarity(\n", " original_768[:100], val_embs[:100], dim=-1).mean().item()\n", " geo_cv = cv_metric(F.normalize(geo_context[:2000], dim=-1))\n", " S = torch.linalg.svdvals(\n", " geo_context[:2000].float() - geo_context[:2000].float().mean(0))\n", " geo_eff_dim = float((S.sum() ** 2) / (S.pow(2).sum() + 1e-12))\n", " emb_cv = cv_metric(val_embs[:2000])\n", "\n", " print(f\" Passthrough: {passthrough:.6f}\")\n", " print(f\" Emb CV: {emb_cv:.4f} (consensus: {consensus_stats['cv']:.4f})\")\n", " print(f\" Geo context CV: {geo_cv:.4f}\")\n", " print(f\" Geo eff_dim: {geo_eff_dim:.1f} / {D_BANK}\")\n", " print(f\" Expert cos: {v_aux['expert_cos_mean']:.3f} ± {v_aux['expert_cos_std']:.3f}\")\n", " print(f\" Anchor max cos: {v_aux['anchor_max_cos']:.3f}\")\n", " print(f\" Cross-expert: {v_aux['cross_expert_cos']:.4f} ± {v_aux['cross_expert_cos_std']:.4f}\")\n", " print(f\" Disagree ratio: {v_aux['disagreement_ratio']:.6f}\")\n", "\n", " # ── Phase 5: Classifier Test ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 5: CLASSIFIER STABILITY TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " with torch.no_grad():\n", " embs = val_embs[:2000]\n", " sim = embs @ embs.T; sim.fill_diagonal_(-1)\n", " n_pairs = 5000\n", " idx_a = torch.randint(0, 2000, (n_pairs,))\n", " idx_b = torch.randint(0, 2000, (n_pairs,))\n", " pair_cos = sim[idx_a, idx_b]\n", " sorted_cos, _ = pair_cos.sort()\n", " t1 = sorted_cos[n_pairs // 3].item()\n", " t2 = sorted_cos[2 * n_pairs // 3].item()\n", " labels = torch.zeros(n_pairs, dtype=torch.long, device=DEVICE)\n", " labels[pair_cos > t2] = 0\n", " labels[(pair_cos <= t2) & (pair_cos > t1)] = 1\n", " labels[pair_cos <= t1] = 2\n", " enriched_a, _ = bank(embs[idx_a])\n", " enriched_b, _ = bank(embs[idx_b])\n", " a_emb = embs[idx_a]; b_emb = embs[idx_b]\n", " a_geo = enriched_a[:, 768:]; b_geo = enriched_b[:, 768:]\n", " geo_explicit = torch.cat([\n", " F.cosine_similarity(a_emb, b_emb, dim=-1).unsqueeze(-1),\n", " (a_emb - b_emb).pow(2).mean(dim=-1).unsqueeze(-1),\n", " F.cosine_similarity(a_geo, b_geo, dim=-1).unsqueeze(-1),\n", " (a_geo - b_geo).pow(2).mean(dim=-1).unsqueeze(-1),\n", " torch.abs(a_emb - b_emb).mean(dim=-1).unsqueeze(-1),\n", " (a_emb * b_emb).sum(dim=-1).unsqueeze(-1),\n", " ], dim=-1)\n", "\n", " modes = {\n", " \"raw_768\": torch.cat([a_emb, b_emb], dim=-1),\n", " \"raw+diff\": torch.cat([a_emb, b_emb, torch.abs(a_emb - b_emb), a_emb * b_emb], dim=-1),\n", " \"bank_enriched\": torch.cat([enriched_a, enriched_b], dim=-1),\n", " \"bank+diff\": torch.cat([enriched_a, enriched_b,\n", " torch.abs(enriched_a - enriched_b),\n", " enriched_a * enriched_b], dim=-1),\n", " \"geo_explicit\": geo_explicit,\n", " }\n", "\n", " print(f\"\\n {'Mode':<20} {'Dim':>6} {'Train':>7} {'Val':>7} {'Gap':>7}\")\n", " print(f\" {'-'*50}\")\n", "\n", " n_clf_train = 4000\n", " for mode_name, features in modes.items():\n", " feat_dim = features.shape[1]\n", " clf = nn.Sequential(\n", " nn.Linear(feat_dim, min(256, feat_dim)), nn.GELU(),\n", " nn.LayerNorm(min(256, feat_dim)), nn.Dropout(0.1),\n", " nn.Linear(min(256, feat_dim), 3)).to(DEVICE)\n", " clf_opt = torch.optim.Adam(clf.parameters(), lr=1e-3)\n", " train_f = features[:n_clf_train].detach()\n", " train_l = labels[:n_clf_train]\n", " val_f = features[n_clf_train:].detach()\n", " val_l = labels[n_clf_train:]\n", " for e in range(30):\n", " clf.train()\n", " loss = F.cross_entropy(clf(train_f), train_l)\n", " loss.backward(); clf_opt.step(); clf_opt.zero_grad()\n", " clf.eval()\n", " with torch.no_grad():\n", " v_acc = (clf(val_f).argmax(-1) == val_l).float().mean().item()\n", " t_acc = (clf(train_f).argmax(-1) == train_l).float().mean().item()\n", " print(f\" {mode_name:<20} {feat_dim:>6} {t_acc:>7.3f} {v_acc:>7.3f} {t_acc-v_acc:>7.3f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Consensus CV: {consensus_stats['cv']:.4f}\")\n", " print(f\" Consensus eff_dim: {consensus_stats['eff_dim']:.1f}\")\n", " print(f\" Equidistance: {equidist:.4f}\")\n", " print(f\" Bank params: {bank_params:,}\")\n", " print(f\" Bank geo eff_dim: {geo_eff_dim:.1f}\")\n", " print(f\" Bank geo CV: {geo_cv:.4f}\")\n", " print(f\" Best val loss: {best_v_loss:.4f}\")\n", " print(f\"\\n Files: alignment_bank_best.pt, alignment_bank_final.pt\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "b63e64cfb73141dca1082ac02358b288", "fa1545986ad94525a89ee64cd3ed2341", "62da4d748f644d36a45c1a9306c226f2", "1da92130d7114b55a8a4fc691337691e", "3e8ee8649aef4331af229d584a6bccba", "1b06a9738e444782b7885db78dfd30c9", "1114ece9b9884973a790259962983edb", "ae2c80f5d3db4b5f8b9748a34ea27906", "e3c8e44dcd0e4b3b84a5c60493e8684e", "0fa8053b5f6340a49ed293ca538c41e0", "00dfa9645b234496be78e8f8e58c8e38", "2dcc8c0441bb4966a6c9ec3b810c06fe", "e83df993ae8c4e26a9aef7c2ecafc810", "370d7642e679416690f7380ae3ba03b4", "2ece17832e524e80a1740571ea0557f8", "1ab761ee76294cb1a049a3c672bf47af", "da9b05cbed414810bab33c39f192f7e8", "ed090236ac4a42598beb85e2916abe1e", "85c3b5826c104280bb2a3483f87bcae3", "5c0e201b8dfb4db4a587d1dc0046089f", "ca439a1c8d9a42f1b5a3cce4e936b60d", "b7fea8a491a84fb99d7e1aea9fc7f61f", "c73d64f12b3547fc8cd59b4598812465", "602a23aaf0a4401aa5e3c9263f6026c5", "170719a38be14c138a17a4a9c315104d", "3e7a63e286344af1ba9a49e7ca55380c", "a5bb395b6b5c4c6b9008ae7efbe5f879", "b00700760d5744a3abcecda5c56a6a37", "7e2d5b90cb674b0aade480ca52539ab9", "06d1a27817564802bd85b5144a8f66b1", "c4885af32a6f45e682a15bd7b2681404", "88a1cc0f868041bf97319dc4eb14965c", "f78341ea0b984611ab0320ada1474f00", "3d49c72bf02f46d99659693617d02bad", "a41e39e89be040b79b23ffa3c90fb41f", "c2d339a959a94b3b893172e6b79a46f4", "9a5b2d6e52b34c5e827f271d159ae4d1", 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dim: 128\n", "\n", "=================================================================\n", "PHASE 0: EXPERT EMBEDDINGS\n", "=================================================================\n", " Loading 500,000 captions...\n", " Got 500,000 captions\n", "\n", " Extracting: bert (google-bert/bert-base-uncased, max_len=512)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00= 3:\n", " bank_sections.add(f\"bank.{parts[1]}\")\n", " else:\n", " bank_sections.add(k)\n", "for s in sorted(bank_sections):\n", " keys_in = [k for k in bank_keys if k.startswith(s)]\n", " params_in = sum(fused[k].numel() for k in keys_in)\n", " print(f\" {s}: {len(keys_in)} tensors, {params_in:,} params\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# 4. SAVE FUSED model.safetensors\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "sf_path = \"/tmp/model.safetensors\"\n", "safetensors_save(fused, sf_path)\n", "size_mb = os.path.getsize(sf_path) / 1e6\n", "print(f\"\\n✓ model.safetensors ({size_mb:.1f} MB)\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# 5. VERIFY LOAD\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"\\n Verifying fused model loads...\")\n", "import sys\n", "# Use the updated modeling code\n", "modeling_path = None\n", "for p in [\"/mnt/user-data/outputs/modeling_caption_bert.py\",\n", " \"modeling_caption_bert.py\"]:\n", " if os.path.exists(p):\n", " modeling_path = p\n", " break\n", "\n", "if modeling_path:\n", " sys.path.insert(0, os.path.dirname(os.path.abspath(modeling_path)))\n", " from modeling_caption_bert import CaptionBertConfig, CaptionBertModel\n", " from safetensors.torch import load_file\n", "\n", " config_path = None\n", " for p in [\"/mnt/user-data/outputs/config_captionbert.json\",\n", " \"config_captionbert.json\"]:\n", " if os.path.exists(p):\n", " config_path = p\n", " break\n", "\n", " with open(config_path) as f:\n", " cfg_dict = json.load(f)\n", "\n", " # Remove non-config keys\n", " for k in [\"auto_map\", \"architectures\", \"tokenizer_class\",\n", " \"torch_dtype\", \"transformers_version\",\n", " \"consensus_models\", \"consensus_alignment\",\n", " \"consensus_equidistance\", \"training_data\", \"training_samples\"]:\n", " cfg_dict.pop(k, None)\n", "\n", " config = CaptionBertConfig(**cfg_dict)\n", " model = CaptionBertModel(config)\n", "\n", " state = load_file(sf_path)\n", " missing, unexpected = model.load_state_dict(state, strict=False)\n", " print(f\" Missing: {len(missing)} {missing[:3] if missing else '[]'}\")\n", " print(f\" Unexpected: {len(unexpected)} {unexpected[:3] if unexpected else '[]'}\")\n", "\n", " # Test forward\n", " tok = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", " inputs = tok([\"A cat on a windowsill\", \"A dog on the beach\"],\n", " max_length=128, padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\")\n", " with torch.no_grad():\n", " out = model(**inputs)\n", "\n", " print(f\" last_hidden_state: {out.last_hidden_state.shape}\")\n", " print(f\" enriched: {out.enriched.shape if out.enriched is not None else 'None'}\")\n", " print(f\" token_embeddings: {out.token_embeddings.shape}\")\n", " if out.geometric_context:\n", " print(f\" geometric_context: {list(out.geometric_context.keys())}\")\n", "\n", " cos = (out.last_hidden_state[0] @ out.last_hidden_state[1]).item()\n", " print(f\" cat↔dog cosine: {cos:.3f}\")\n", "\n", " if out.enriched is not None:\n", " print(f\" enriched dim: {out.enriched.shape[1]} (768 embed + {out.enriched.shape[1]-768} bank)\")\n", "\n", " print(\" ✓ Verification passed\")\n", " del model\n", "else:\n", " print(\" ⚠ Could not find modeling_caption_bert.py for verification\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# 6. UPLOAD EVERYTHING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(f\"UPLOADING TO {REPO_ID}\")\n", "print(f\"{'='*65}\")\n", "\n", "# 6a. model.safetensors (fused encoder + bank)\n", "api.upload_file(path_or_fileobj=sf_path,\n", " path_in_repo=\"model.safetensors\", repo_id=REPO_ID)\n", "print(f\"✓ model.safetensors ({size_mb:.1f} MB)\")\n", "\n", "# 6b. Bank standalone checkpoint\n", "if bank_path:\n", " api.upload_file(path_or_fileobj=bank_path,\n", " path_in_repo=\"checkpoints/alignment_bank_best.pt\",\n", " repo_id=REPO_ID)\n", " print(f\"✓ checkpoints/alignment_bank_best.pt\")\n", " # Also upload final if exists\n", " if os.path.exists(\"alignment_bank_final.pt\"):\n", " api.upload_file(path_or_fileobj=\"alignment_bank_final.pt\",\n", " path_in_repo=\"checkpoints/alignment_bank_final.pt\",\n", " repo_id=REPO_ID)\n", " print(\"✓ checkpoints/alignment_bank_final.pt\")\n", "\n", "# 6e. Encoder standalone (for continued training without bank)\n", "api.upload_file(path_or_fileobj=encoder_path,\n", " path_in_repo=\"checkpoints/encoder_best.pt\",\n", " repo_id=REPO_ID)\n", "print(\"✓ checkpoints/encoder_best.pt\")\n", "\n", "# 6f. 500K cache\n", "print(\"\\n Uploading consensus_500k cache...\")\n", "cache_files = {\n", " \"bert.pt\": \"cache_consensus_500k/bert.pt\",\n", " \"modern.pt\": \"cache_consensus_500k/modern.pt\",\n", " \"roberta.pt\": \"cache_consensus_500k/roberta.pt\",\n", " \"albert.pt\": \"cache_consensus_500k/albert.pt\",\n", " \"distil.pt\": \"cache_consensus_500k/distil.pt\",\n", " \"captions.json\": \"cache_consensus_500k/captions.json\",\n", "}\n", "for local_name, repo_path in cache_files.items():\n", " local_path = os.path.join(CACHE_DIR, local_name)\n", " if os.path.exists(local_path):\n", " size = os.path.getsize(local_path) / 1e6\n", " api.upload_file(path_or_fileobj=local_path,\n", " path_in_repo=repo_path, repo_id=REPO_ID)\n", " print(f\" ✓ {repo_path} ({size:.1f} MB)\")\n", " else:\n", " print(f\" ⚠ {local_path} not found, skipping\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# 7. VERIFY REPO\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(f\"REPO CONTENTS\")\n", "print(f\"{'='*65}\")\n", "info = api.model_info(REPO_ID)\n", "for s in sorted(info.siblings, key=lambda x: x.rfilename):\n", " size = f\"({s.size/1e6:.1f} MB)\" if s.size and s.size > 100000 else \"\"\n", " print(f\" {s.rfilename} {size}\")\n", "\n", "print(f\"\\nhttps://huggingface.co/{REPO_ID}\")\n", "print(f\"\\nUsage:\")\n", "print(f' model = AutoModel.from_pretrained(\"{REPO_ID}\", trust_remote_code=True)')\n", "print(f' tokenizer = AutoTokenizer.from_pretrained(\"{REPO_ID}\", trust_remote_code=True)')\n", "print(f\" out = model(**tokenizer('A cat', return_tensors='pt', padding=True))\")\n", "print(f\" embedding = out.last_hidden_state # (1, 768)\")\n", "print(f\" enriched = out.enriched # (1, 896)\")\n", "print(f\" geo = out.geometric_context # dict\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", 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"65bb38bd08cd449b968dea79f08fd027", "796a0b14aa4144fe94ca38f09620e5f1", "3e477950d41e4d689207fe4de3da3aa8", "28d93f9b274e428782b6be59665f9984", "8dcebf5b9b0043b09f3eae87e066798d", "95f6f2f2332d4537860fe746b8a4bd47", "4870ff7175c34edbaa2f20fe54d2929a" ] }, "id": "go985XH3gse6", "outputId": "d41eda8a-6436-4ab8-b25a-9d81d0e2b419" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "UPLOAD: AbstractPhil/geolip-captionbert-8192\n", "=================================================================\n", " Downloading encoder weights from HF...\n", " Encoder: 82 tensors from /root/.cache/huggingface/hub/models--AbstractPhil--geolip-captionbert-8192/snapshots/8dbe9e65befe677a679e9b4b47e743d88d5e6e1a/best_model.pt\n", " Encoder params: 25,958,016\n", " Bank: 30 tensors from alignment_bank_best.pt\n", " Bank params: 6,466,999\n", "\n", " Fused state dict: 112 tensors, 32,425,015 params\n", " Encoder keys: 82\n", " Bank keys: 30\n", " bank.anchors: 1 tensors, 393,216 params\n", " bank.expert_means: 5 tensors, 3,840 params\n", " bank.expert_rotations: 5 tensors, 2,949,120 params\n", " bank.expert_whiteners: 5 tensors, 2,949,120 params\n", " bank.geo_proj: 8 tensors, 171,648 params\n", " bank.target_cross_cos_mean: 1 tensors, 1 params\n", " bank.target_cross_cos_std: 1 tensors, 1 params\n", " bank.target_cv: 1 tensors, 1 params\n", " bank.target_disagreement_ratio: 1 tensors, 1 params\n", " bank.target_mean_cos: 1 tensors, 1 params\n", " bank.target_spectral: 1 tensors, 50 params\n", "\n", "✓ model.safetensors (129.7 MB)\n", "\n", " Verifying fused model loads...\n", " ⚠ Could not find modeling_caption_bert.py for verification\n", "\n", "=================================================================\n", "UPLOADING TO AbstractPhil/geolip-captionbert-8192\n", "=================================================================\n" ] }, { "output_type": 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"display_data", "data": { "text/plain": [ " ...sensus_500k/captions.json: 3%|2 | 6.73MB / 232MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "fa30381c3e2a48c6a2d2738173667d63" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " ✓ cache_consensus_500k/captions.json (232.4 MB)\n", "\n", "=================================================================\n", "REPO CONTENTS\n", "=================================================================\n", " .gitattributes \n", " README.md \n", " benchmarks/benchmark_post.py \n", " benchmarks/captionbert_8192_post_upgrade_60epoch_5test.json \n", " benchmarks/captionbert_8192_post_upgrade_60epoch_5test_output.txt \n", " benchmarks/early_bench.py \n", " best_model.pt \n", " cache_consensus_500k/albert.pt \n", " cache_consensus_500k/bert.pt \n", " cache_consensus_500k/captions.json \n", " cache_consensus_500k/distil.pt \n", " cache_consensus_500k/modern.pt \n", " cache_consensus_500k/roberta.pt \n", " checkpoints/alignment_bank_best.pt \n", " checkpoints/alignment_bank_final.pt \n", " checkpoints/encoder_best.pt \n", " checkpoints/model_e10.pt \n", " checkpoints/model_e20.pt \n", " checkpoints/model_e30.pt \n", " colab_deep_analysis.py \n", " colab_test_script.py \n", " config.json \n", " final_trains/base_best_model_e30.pt \n", " final_trains/base_final_model_e60.pt \n", " model.safetensors \n", " modeling_caption_bert.py \n", " tokenizer.json \n", " tokenizer/tokenizer.json \n", " tokenizer/tokenizer_config.json \n", " tokenizer_config.json \n", " trainers/nil_head_trainer_cross_entropy_fail.py \n", " trainers/nil_head_trainer_full_geometric_losses_CE_fail.py \n", " trainers/trainer_alignment_base.py \n", " trainers/trainer_alignment_base_8192_upgrade.py \n", " trainers/trainer_anchor_bank_attempt_1.py \n", " training_metrics/bank_training_try1_500k_output.txt \n", " training_metrics/metrics.json \n", "\n", "https://huggingface.co/AbstractPhil/geolip-captionbert-8192\n", "\n", "Usage:\n", " model = AutoModel.from_pretrained(\"AbstractPhil/geolip-captionbert-8192\", trust_remote_code=True)\n", " tokenizer = AutoTokenizer.from_pretrained(\"AbstractPhil/geolip-captionbert-8192\", trust_remote_code=True)\n", " out = model(**tokenizer('A cat', return_tensors='pt', padding=True))\n", " embedding = out.last_hidden_state # (1, 768)\n", " enriched = out.enriched # (1, 896)\n", " geo = out.geometric_context # dict\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# INFERENCE TEST: CaptionBERT-8192 with Alignment Bank\n", "# ============================================================================\n", "\n", "from transformers import AutoModel, AutoTokenizer\n", "import torch\n", "\n", "REPO_ID = \"AbstractPhil/geolip-captionbert-8192\"\n", "\n", "print(\"Loading model...\")\n", "model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True)\n", "model.eval()\n", "print(f\" Parameters: {sum(p.numel() for p in model.parameters()):,}\")\n", "print(f\" Bank: {'present' if hasattr(model, 'bank') and model.bank is not None else 'MISSING'}\")\n", "\n", "print(\"Loading tokenizer...\")\n", "tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", "print(f\" Vocab: {tokenizer.vocab_size}\")\n", "\n", "texts = [\n", " \"A cat sitting on a windowsill watching birds outside\",\n", " \"A golden retriever playing fetch on the beach at sunset\",\n", " \"A still life painting with flowers and fruit on a table\",\n", " \"An aerial photograph of a city skyline at night\",\n", " \"A child riding a bicycle through autumn leaves in a park\",\n", " \"a girl performing an action\",\n", " \"a boy performing an action\",\n", " \"a woman performing an action\",\n", " \"a man performing an action\",\n", "]\n", "\n", "inputs = tokenizer(texts, max_length=8192, padding=True,\n", " truncation=True, return_tensors=\"pt\")\n", "\n", "with torch.no_grad():\n", " outputs = model(**inputs)\n", "\n", "emb = outputs.last_hidden_state\n", "print(f\"\\n Embedding: {emb.shape}\")\n", "print(f\" Norms: {[f'{n:.4f}' for n in emb.norm(dim=-1).tolist()]}\")\n", "\n", "if outputs.enriched is not None:\n", " print(f\" Enriched: {outputs.enriched.shape} (768 + {outputs.enriched.shape[1] - 768} bank)\")\n", "else:\n", " print(f\" Enriched: None (bank not loaded)\")\n", "\n", "print(f\" Tokens: {outputs.token_embeddings.shape}\")\n", "\n", "if outputs.geometric_context:\n", " print(f\"\\n Geometric context:\")\n", " for k, v in outputs.geometric_context.items():\n", " print(f\" {k}: {v:.4f}\" if isinstance(v, float) else f\" {k}: {v}\")\n", "\n", "print(f\"\\n Pairwise cosine similarity:\")\n", "sim = emb @ emb.T\n", "for i in range(len(texts)):\n", " for j in range(i+1, len(texts)):\n", " print(f\" [{i}]↔[{j}]: {sim[i,j]:.3f} ({texts[i][:40]}↔{texts[j][:40]})\")\n", "\n", "if outputs.enriched is not None:\n", " enr = outputs.enriched\n", " enr_sim = torch.cosine_similarity(enr.unsqueeze(1), enr.unsqueeze(0), dim=-1)\n", " print(f\"\\n Enriched pairwise (768+bank):\")\n", " for i in range(5):\n", " for j in range(i+1, 5):\n", " delta = enr_sim[i,j].item() - sim[i,j].item()\n", " print(f\" [{i}]↔[{j}]: {enr_sim[i,j]:.3f} (Δ={delta:+.3f} from raw)\")\n", "\n", "if hasattr(model, 'encode'):\n", " e = model.encode([\"Hello world\", \"Testing the encoder\"], tokenizer=tokenizer)\n", " print(f\"\\n encode() output: {e.shape}\")\n", " print(f\" encode() cosine: {(e[0] @ e[1]).item():.3f}\")\n", "\n", "print(\"\\n✓ All tests passed\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "f46f04d5073945e2add5ac5a82747534", "36d2eeeb7d6d43e2802991a7ee948df8", "12d840fff521422eade1ce9792fed91f", "d31a9a5560d84d5eb94f994682f0454a", "b7077c26d0004b82ae2c8b1065804188", "5bf5ef0ca2514e5595ed03f2b3d32dd9", "1d3d8ca8a3854b0a83db4df3c15f9a6d", "1d63c2f6c3d641b9b041a0e5142ea891", "bfcda213611c4943859fc5b8b309a31b", "3ebfa3ed6cd94159bd339766e3a6bb47", "329e3afa542d4c788695e60cb541d4a4", "ea3b041d08e7434498a37af80aed30c3", "ed5026404e97440e948c9207fc80eaa6", "8a12c418435c4446ae67db9a0285b27b", "630ab1aa01bb4b26b430a68ab996a35f", "b7b33f7e5d4b445a958fc936350bdd33", "288a439304574b978a1a169c934b4f4f", "b521d31ddc214bc8a73499fb947615d3", "85c078ae16134042996a13d1585ac24f", "ae5bd77af032451b8d21687f60c4f177", "0ea428f6de0d45bd8394be630f8e1d93", "a1316b6b0692402e9127f9eec611c6a1" ] }, "id": "HHyjutMqjHnn", "outputId": "3655eb1c-a4bd-4582-aef5-d17a9a352bc7" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading model...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "A new version of the following files was downloaded from https://huggingface.co/AbstractPhil/geolip-captionbert-8192:\n", "- modeling_caption_bert.py\n", ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/130M [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " from transformers import AutoModel, AutoTokenizer\n", " from datasets import load_dataset\n", "\n", " # ── Load model ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING MODEL\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", " for p in model.parameters():\n", " p.requires_grad = False\n", "\n", " has_bank = model.bank is not None\n", " print(f\" Model: {sum(p.numel() for p in model.parameters()):,} params (frozen)\")\n", " print(f\" Bank: {'present' if has_bank else 'absent'}\")\n", "\n", " # ── Load SNLI ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING SNLI\")\n", " print(f\"{'='*65}\")\n", "\n", " ds = load_dataset(\"stanfordnlp/snli\")\n", " train_ds = ds[\"train\"].filter(lambda x: x[\"label\"] >= 0)\n", " val_ds = ds[\"validation\"].filter(lambda x: x[\"label\"] >= 0)\n", " print(f\" Train: {len(train_ds):,} Val: {len(val_ds):,}\")\n", "\n", " MAX_TRAIN = 549000 # full SNLI\n", " MAX_VAL = 9800\n", "\n", " # ── Pre-encode ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PRE-ENCODING\")\n", " print(f\"{'='*65}\")\n", "\n", " @torch.no_grad()\n", " def encode_pairs(dataset, max_n, batch_size=256):\n", " dataset = dataset.select(range(min(max_n, len(dataset))))\n", " all_p_enr, all_h_enr = [], []\n", " all_labels = []\n", "\n", " for i in tqdm(range(0, len(dataset), batch_size), desc=\" Encoding\"):\n", " j = min(i + batch_size, len(dataset))\n", " batch = dataset[i:j]\n", "\n", " p_in = tokenizer(batch[\"premise\"], max_length=128,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_in)\n", "\n", " h_in = tokenizer(batch[\"hypothesis\"], max_length=128,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " h_out = model(**h_in)\n", "\n", " p_feat = p_out.enriched if p_out.enriched is not None else p_out.last_hidden_state\n", " h_feat = h_out.enriched if h_out.enriched is not None else h_out.last_hidden_state\n", "\n", " all_p_enr.append(p_feat.cpu())\n", " all_h_enr.append(h_feat.cpu())\n", " all_labels.append(torch.tensor(batch[\"label\"]))\n", "\n", " return {\n", " \"p\": torch.cat(all_p_enr),\n", " \"h\": torch.cat(all_h_enr),\n", " \"labels\": torch.cat(all_labels),\n", " }\n", "\n", " train_data = encode_pairs(train_ds, MAX_TRAIN)\n", " val_data = encode_pairs(val_ds, MAX_VAL)\n", "\n", " d_enriched = train_data[\"p\"].shape[1]\n", " d_raw = 768\n", " d_bank = d_enriched - d_raw\n", " print(f\" Enriched: {d_enriched} (raw={d_raw} + bank={d_bank})\")\n", " print(f\" Train: {train_data['labels'].shape[0]:,} Val: {val_data['labels'].shape[0]:,}\")\n", "\n", " for label, name in [(0, \"entailment\"), (1, \"neutral\"), (2, \"contradiction\")]:\n", " n_l = (train_data[\"labels\"] == label).sum().item()\n", " print(f\" {name}: {n_l:,} ({n_l/len(train_data['labels']):.1%})\")\n", "\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # Move to GPU\n", " train_p = train_data[\"p\"].to(DEVICE)\n", " train_h = train_data[\"h\"].to(DEVICE)\n", " train_labels = train_data[\"labels\"].to(DEVICE)\n", " val_p = val_data[\"p\"].to(DEVICE)\n", " val_h = val_data[\"h\"].to(DEVICE)\n", " val_labels = val_data[\"labels\"].to(DEVICE)\n", "\n", " # ── Build CompConv NLI ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"COMPOSITIONAL CONV NLI HEAD\")\n", " print(f\"{'='*65}\")\n", "\n", " nli = CompConvNLI(\n", " d_raw=d_raw, d_bank=max(d_bank, 1),\n", " d_path=128, n_components=5, n_classes=3, dropout=0.1\n", " ).to(DEVICE)\n", " n_head_params = sum(p.numel() for p in nli.parameters())\n", " print(f\" Head params: {n_head_params:,}\")\n", "\n", " # ── Training ──\n", " EPOCHS = 10\n", " BATCH = 512\n", " LR = 5e-4\n", " n_train = train_labels.shape[0]\n", " n_batches = n_train // BATCH\n", "\n", " optimizer = torch.optim.AdamW(nli.parameters(), lr=LR, weight_decay=0.01)\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=n_batches * EPOCHS, eta_min=1e-6)\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({EPOCHS} epochs, {n_batches} batches/epoch)\")\n", " print(f\"{'='*65}\")\n", "\n", " best_val_acc = 0.0\n", " for epoch in range(EPOCHS):\n", " nli.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", "\n", " logits, _ = nli(train_p[idx], train_h[idx])\n", " labels = train_labels[idx]\n", " loss = F.cross_entropy(logits, labels)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(nli.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += len(idx)\n", "\n", " elapsed = time.time() - t0\n", " train_acc = total_correct / max(n, 1)\n", " train_loss = total_loss / max(n // BATCH, 1)\n", "\n", " # Validation\n", " nli.eval()\n", " with torch.no_grad():\n", " val_n = val_labels.shape[0]\n", " val_correct = 0\n", " val_loss_sum = 0\n", " all_preds, all_labs = [], []\n", " path_info = None\n", "\n", " for i in range(0, val_n, 512):\n", " j = min(i + 512, val_n)\n", " logits, info = nli(val_p[i:j], val_h[i:j])\n", " labs = val_labels[i:j]\n", " val_correct += (logits.argmax(-1) == labs).sum().item()\n", " val_loss_sum += F.cross_entropy(logits, labs, reduction=\"sum\").item()\n", " all_preds.append(logits.argmax(-1).cpu())\n", " all_labs.append(labs.cpu())\n", " if path_info is None:\n", " path_info = info\n", "\n", " val_acc = val_correct / val_n\n", " val_loss = val_loss_sum / val_n\n", " preds = torch.cat(all_preds)\n", " labs_all = torch.cat(all_labs)\n", "\n", " acc_ent = (preds[labs_all == 0] == 0).float().mean().item() if (labs_all == 0).sum() > 0 else 0\n", " acc_neu = (preds[labs_all == 1] == 1).float().mean().item() if (labs_all == 1).sum() > 0 else 0\n", " acc_con = (preds[labs_all == 2] == 2).float().mean().item() if (labs_all == 2).sum() > 0 else 0\n", "\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s\")\n", " print(f\" Task: loss={train_loss:.4f} t_acc={train_acc:.4f} v_acc={val_acc:.4f} v_loss={val_loss:.4f}\")\n", " print(f\" Per-class: ent={acc_ent:.3f} neu={acc_neu:.3f} con={acc_con:.3f}\")\n", " if path_info:\n", " top3 = path_info[\"top_paths\"][:3]\n", " path_str = \" \".join(f\"{comp}={w:.3f}\" for comp, w in top3)\n", " print(f\" Paths: {path_str} spread={path_info['weight_spread']:.4f}\")\n", " print(f\" Protos: sim={path_info.get('proto_spread', 0):.4f} \"\n", " f\"temp={path_info.get('temperature', 0):.2f}\")\n", "\n", " if val_acc > best_val_acc:\n", " best_val_acc = val_acc\n", " torch.save(nli.state_dict(), \"nli_conv5d_best.pt\")\n", " print(f\" ★ New best: {val_acc:.4f}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # PATH ANALYSIS\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"PATH WEIGHT ANALYSIS\")\n", " print(f\"{'='*65}\")\n", "\n", " nli.load_state_dict(torch.load(\"nli_conv5d_best.pt\", weights_only=True))\n", " nli.eval()\n", "\n", " weights = F.softmax(nli.path_weights, dim=0).cpu().tolist()\n", " ranked = sorted(zip(nli.compositions, weights), key=lambda x: -x[1])\n", " print(f\"\\n {'Path':<25} {'Weight':>8} {'Type':<15}\")\n", " print(f\" {'-'*50}\")\n", " for comp, w in ranked:\n", " if len(comp) == 1:\n", " ptype = \"holistic\"\n", " elif all(c == 1 for c in comp):\n", " ptype = \"independent\"\n", " elif comp[0] >= 3:\n", " ptype = \"premise-heavy\"\n", " else:\n", " ptype = \"mixed\"\n", " bar = \"█\" * int(w * 100)\n", " print(f\" {str(comp):<25} {w:>8.4f} {ptype:<15} {bar}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # COMPOSITIONAL ORDER TEST\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"COMPOSITIONAL ORDER TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", " label_names = [\"entailment\", \"neutral\", \"contradiction\"]\n", "\n", " test_pairs = [\n", " (\"a potato on top of a table\", \"a table on top of a potato\"),\n", " (\"a potato on top of a table\", \"there is a potato\"),\n", " (\"a cat is sitting on a mat\", \"a mat is sitting on a cat\"),\n", " (\"a dog chased the cat\", \"the cat chased the dog\"),\n", " (\"a woman is holding a baby\", \"a baby is holding a woman\"),\n", " (\"the boy kicked the ball\", \"the ball kicked the boy\"),\n", " (\"a man is riding a horse\", \"a horse is riding a man\"),\n", " (\"a girl is painting a picture\", \"a girl is creating art\"),\n", " (\"two dogs are playing in a park\", \"animals are outdoors\"),\n", " (\"a person is swimming in the ocean\", \"nobody is in the water\"),\n", " ]\n", "\n", " with torch.no_grad():\n", " for premise, hypothesis in test_pairs:\n", " p_in = tokenizer([premise], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " h_in = tokenizer([hypothesis], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_in)\n", " h_out = model(**h_in)\n", "\n", " p_feat = p_out.enriched if p_out.enriched is not None else p_out.last_hidden_state\n", " h_feat = h_out.enriched if h_out.enriched is not None else h_out.last_hidden_state\n", "\n", " logits, _ = nli(p_feat, h_feat)\n", " probs = F.softmax(logits, dim=-1)[0]\n", " pred = label_names[probs.argmax()]\n", "\n", " cos = F.cosine_similarity(\n", " p_out.last_hidden_state, h_out.last_hidden_state).item()\n", "\n", " print(f\"\\n P: {premise}\")\n", " print(f\" H: {hypothesis}\")\n", " print(f\" Pooled cos: {cos:.3f}\")\n", " print(f\" NLI: {pred} [E={probs[0]:.3f} N={probs[1]:.3f} C={probs[2]:.3f}]\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Best val accuracy: {best_val_acc:.4f}\")\n", " print(f\" Head params: {n_head_params:,}\")\n", " print(f\" Paths: {len(nli.compositions)}\")\n", " print(f\" Components: {nli.n_components} → d_path={nli.d_path}\")\n", " print(f\" Bank present: {has_bank}\")\n", " print(f\" Saved: nli_conv5d_best.pt\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "aaa1014322ee46b583d42f9c14926b69", "5a2fc8ba9e1f40358f2ff2fd5a2417fb", "699abe3252ac4a0eae550e70da47c4a8", "d02dd92a6d854d0e8bcb67d313998f4a", "a55ca004ee9641f8ade3bfa17b5aaa72", "5203c99f5dce4b928172907f12fa3b66", "f4bcfcda00c64a65b6b823d0f8395e1a", "eb4d1532bbec441f877e19215516afa0", "5fc8bc04e2214dcf81eb2524d533065e", "2b7be4a063034e94aacabc7fad352d3d", "5edb67365a5c4bef9c248a99febf42ca", "db10dfddd4e942e1b64e0cda2d3aee64", "c0396e0a06474078a37a6a84a19d8ce9", "86ca7df7efda4534b017d17af36fb0e4", "6a0cecf170354796ad0e64b6a1073c8b", "085300d4121a445eaab1864861967ebf", "aa4dd6dfb06242f19a25050e4eaacf4a", "87cd43cc1bc140f99a0f826066a84a4e", "70bfc00d11ef4b2aa607b449abf6b27f", "6c6d9068a112407999016a2e90339756", "538cdeaff560479ab2703f3810c4a69d", "de747149f30a42c49fb8a11ed518364b" ] }, "id": "s7gNF3zImn27", "outputId": "4f286a88-b76f-454b-b36c-1a8801b6f01a" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "NLI HEAD: Compositional Convolution (conv5d)\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "LOADING MODEL\n", "=================================================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/112 [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " from transformers import AutoModel, AutoTokenizer\n", " from datasets import load_dataset\n", "\n", " # ── Load model ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING MODEL\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", " for p in model.parameters():\n", " p.requires_grad = False\n", "\n", " has_bank = model.bank is not None\n", " print(f\" Model: {sum(p.numel() for p in model.parameters()):,} params (frozen)\")\n", " print(f\" Bank: {'present' if has_bank else 'absent'}\")\n", "\n", " # ── Load SNLI ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING SNLI\")\n", " print(f\"{'='*65}\")\n", "\n", " ds = load_dataset(\"stanfordnlp/snli\")\n", " train_ds = ds[\"train\"].filter(lambda x: x[\"label\"] >= 0)\n", " val_ds = ds[\"validation\"].filter(lambda x: x[\"label\"] >= 0)\n", " print(f\" Train: {len(train_ds):,} Val: {len(val_ds):,}\")\n", "\n", " MAX_TRAIN = 549000 # full SNLI\n", " MAX_VAL = 9800\n", "\n", " # ── Pre-encode ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PRE-ENCODING\")\n", " print(f\"{'='*65}\")\n", "\n", " @torch.no_grad()\n", " def encode_pairs(dataset, max_n, batch_size=1024):\n", " dataset = dataset.select(range(min(max_n, len(dataset))))\n", " all_p_enr, all_h_enr = [], []\n", " all_labels = []\n", "\n", " for i in tqdm(range(0, len(dataset), batch_size), desc=\" Encoding\"):\n", " j = min(i + batch_size, len(dataset))\n", " batch = dataset[i:j]\n", "\n", " p_in = tokenizer(batch[\"premise\"], max_length=128,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_in)\n", "\n", " h_in = tokenizer(batch[\"hypothesis\"], max_length=128,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " h_out = model(**h_in)\n", "\n", " p_feat = p_out.enriched if p_out.enriched is not None else p_out.last_hidden_state\n", " h_feat = h_out.enriched if h_out.enriched is not None else h_out.last_hidden_state\n", "\n", " all_p_enr.append(p_feat.cpu())\n", " all_h_enr.append(h_feat.cpu())\n", " all_labels.append(torch.tensor(batch[\"label\"]))\n", "\n", " return {\n", " \"p\": torch.cat(all_p_enr),\n", " \"h\": torch.cat(all_h_enr),\n", " \"labels\": torch.cat(all_labels),\n", " }\n", "\n", " train_data = encode_pairs(train_ds, MAX_TRAIN)\n", " val_data = encode_pairs(val_ds, MAX_VAL)\n", "\n", " d_enriched = train_data[\"p\"].shape[1]\n", " d_raw = 768\n", " d_bank = d_enriched - d_raw\n", " print(f\" Enriched: {d_enriched} (raw={d_raw} + bank={d_bank})\")\n", " print(f\" Train: {train_data['labels'].shape[0]:,} Val: {val_data['labels'].shape[0]:,}\")\n", "\n", " for label, name in [(0, \"entailment\"), (1, \"neutral\"), (2, \"contradiction\")]:\n", " n_l = (train_data[\"labels\"] == label).sum().item()\n", " print(f\" {name}: {n_l:,} ({n_l/len(train_data['labels']):.1%})\")\n", "\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # Move to GPU\n", " train_p = train_data[\"p\"].to(DEVICE)\n", " train_h = train_data[\"h\"].to(DEVICE)\n", " train_labels = train_data[\"labels\"].to(DEVICE)\n", " val_p = val_data[\"p\"].to(DEVICE)\n", " val_h = val_data[\"h\"].to(DEVICE)\n", " val_labels = val_data[\"labels\"].to(DEVICE)\n", "\n", " # ── Build CompConv NLI ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"COMPOSITIONAL CONV NLI HEAD\")\n", " print(f\"{'='*65}\")\n", "\n", " nli = CompConvNLI(\n", " d_raw=d_raw, d_bank=max(d_bank, 1),\n", " #d_path=512,\n", " n_components=5, n_classes=3, dropout=0.3\n", " ).to(DEVICE)\n", " n_head_params = sum(p.numel() for p in nli.parameters())\n", " print(f\" Head params: {n_head_params:,}\")\n", "\n", " # ── Training ──\n", " EPOCHS = 20\n", " BATCH = 128\n", " LR = 1e-4\n", " n_train = train_labels.shape[0]\n", " n_batches = n_train // BATCH\n", "\n", " optimizer = torch.optim.AdamW(nli.parameters(), lr=LR, weight_decay=0.01)\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=n_batches * EPOCHS, eta_min=1e-6)\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({EPOCHS} epochs, {n_batches} batches/epoch)\")\n", " print(f\"{'='*65}\")\n", "\n", " best_val_acc = 0.0\n", " for epoch in range(EPOCHS):\n", " nli.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", "\n", " logits, _ = nli(train_p[idx], train_h[idx])\n", " labels = train_labels[idx]\n", " loss = F.cross_entropy(logits, labels)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(nli.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += len(idx)\n", "\n", " elapsed = time.time() - t0\n", " train_acc = total_correct / max(n, 1)\n", " train_loss = total_loss / max(n // BATCH, 1)\n", "\n", " # Validation\n", " nli.eval()\n", " with torch.no_grad():\n", " val_n = val_labels.shape[0]\n", " val_correct = 0\n", " val_loss_sum = 0\n", " all_preds, all_labs = [], []\n", " path_info = None\n", "\n", " for i in range(0, val_n, 512):\n", " j = min(i + 512, val_n)\n", " logits, info = nli(val_p[i:j], val_h[i:j])\n", " labs = val_labels[i:j]\n", " val_correct += (logits.argmax(-1) == labs).sum().item()\n", " val_loss_sum += F.cross_entropy(logits, labs, reduction=\"sum\").item()\n", " all_preds.append(logits.argmax(-1).cpu())\n", " all_labs.append(labs.cpu())\n", " if path_info is None:\n", " path_info = info\n", "\n", " val_acc = val_correct / val_n\n", " val_loss = val_loss_sum / val_n\n", " preds = torch.cat(all_preds)\n", " labs_all = torch.cat(all_labs)\n", "\n", " acc_ent = (preds[labs_all == 0] == 0).float().mean().item() if (labs_all == 0).sum() > 0 else 0\n", " acc_neu = (preds[labs_all == 1] == 1).float().mean().item() if (labs_all == 1).sum() > 0 else 0\n", " acc_con = (preds[labs_all == 2] == 2).float().mean().item() if (labs_all == 2).sum() > 0 else 0\n", "\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s\")\n", " print(f\" Task: loss={train_loss:.4f} t_acc={train_acc:.4f} v_acc={val_acc:.4f} v_loss={val_loss:.4f}\")\n", " print(f\" Per-class: ent={acc_ent:.3f} neu={acc_neu:.3f} con={acc_con:.3f}\")\n", " if path_info:\n", " top3 = path_info[\"top_paths\"][:3]\n", " path_str = \" \".join(f\"{comp}={w:.3f}\" for comp, w in top3)\n", " print(f\" Paths: {path_str} spread={path_info['weight_spread']:.4f}\")\n", " print(f\" Protos: sim={path_info.get('proto_spread', 0):.4f} \"\n", " f\"temp={path_info.get('temperature', 0):.2f}\")\n", "\n", " if val_acc > best_val_acc:\n", " best_val_acc = val_acc\n", " torch.save(nli.state_dict(), \"nli_conv5d_best.pt\")\n", " print(f\" ★ New best: {val_acc:.4f}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # PATH ANALYSIS\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"PATH WEIGHT ANALYSIS\")\n", " print(f\"{'='*65}\")\n", "\n", " nli.load_state_dict(torch.load(\"nli_conv5d_best.pt\", weights_only=True))\n", " nli.eval()\n", "\n", " weights = F.softmax(nli.path_weights, dim=0).cpu().tolist()\n", " ranked = sorted(zip(nli.compositions, weights), key=lambda x: -x[1])\n", " print(f\"\\n {'Path':<25} {'Weight':>8} {'Type':<15}\")\n", " print(f\" {'-'*50}\")\n", " for comp, w in ranked:\n", " if len(comp) == 1:\n", " ptype = \"holistic\"\n", " elif all(c == 1 for c in comp):\n", " ptype = \"independent\"\n", " elif comp[0] >= 3:\n", " ptype = \"geo-first\"\n", " elif comp[0] == 1 and sum(comp[1:]) == 4:\n", " ptype = \"geo→rest\"\n", " elif comp[0] == 2:\n", " ptype = \"geo+struct→...\"\n", " else:\n", " ptype = \"mixed\"\n", " bar = \"█\" * int(w * 100)\n", " print(f\" {str(comp):<25} {w:>8.4f} {ptype:<15} {bar}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # COMPOSITIONAL ORDER TEST\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"COMPOSITIONAL ORDER TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", " label_names = [\"entailment\", \"neutral\", \"contradiction\"]\n", "\n", " test_pairs = [\n", " (\"a potato on top of a table\", \"a table on top of a potato\"),\n", " (\"a potato on top of a table\", \"there is a potato\"),\n", " (\"a cat is sitting on a mat\", \"a mat is sitting on a cat\"),\n", " (\"a dog chased the cat\", \"the cat chased the dog\"),\n", " (\"a woman is holding a baby\", \"a baby is holding a woman\"),\n", " (\"the boy kicked the ball\", \"the ball kicked the boy\"),\n", " (\"a man is riding a horse\", \"a horse is riding a man\"),\n", " (\"a girl is painting a picture\", \"a girl is creating art\"),\n", " (\"two dogs are playing in a park\", \"animals are outdoors\"),\n", " (\"a person is swimming in the ocean\", \"nobody is in the water\"),\n", " ]\n", "\n", " with torch.no_grad():\n", " for premise, hypothesis in test_pairs:\n", " p_in = tokenizer([premise], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " h_in = tokenizer([hypothesis], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_in)\n", " h_out = model(**h_in)\n", "\n", " p_feat = p_out.enriched if p_out.enriched is not None else p_out.last_hidden_state\n", " h_feat = h_out.enriched if h_out.enriched is not None else h_out.last_hidden_state\n", "\n", " logits, _ = nli(p_feat, h_feat)\n", " probs = F.softmax(logits, dim=-1)[0]\n", " pred = label_names[probs.argmax()]\n", "\n", " cos = F.cosine_similarity(\n", " p_out.last_hidden_state, h_out.last_hidden_state).item()\n", "\n", " print(f\"\\n P: {premise}\")\n", " print(f\" H: {hypothesis}\")\n", " print(f\" Pooled cos: {cos:.3f}\")\n", " print(f\" NLI: {pred} [E={probs[0]:.3f} N={probs[1]:.3f} C={probs[2]:.3f}]\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Best val accuracy: {best_val_acc:.4f}\")\n", " print(f\" Head params: {n_head_params:,}\")\n", " print(f\" Paths: {len(nli.compositions)}\")\n", " print(f\" Components: {nli.n_components} → d_path={nli.d_path}\")\n", " print(f\" Bank present: {has_bank}\")\n", " print(f\" Saved: nli_conv5d_best.pt\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "ef25e8cfba5845d1b4e302267a55af9e", "a45a6e9a5f294549b6fc2526dabfc4f8", "d0ddd09569cb402eb9293b6eb509911e", "d418c749474048318eb434984e396faa", "f3cc72fadce64c89808123ce201e9796", "e4a2d1066d044474803ef1b433b4792f", "67a56aac4c4d40ad8b109b707347ee45", "eb0326c486d748248eaac16f09399b83", "9323c9581fcc45eabf61356cdec62562", "20139b20c7d44c41aa018faf16dc0eee", "783f7928d25e4c40ac67d6a173090928", "b23692bd70a0466a948ed71b0f41a845", "b8edb67d8fe946b2b2f78a8b7b37bcfb", "f6c31ab1a52d4cdfa3dc7528cc0fcf3c", "73c89a1a107c43c58bd14f20defa058f", "7a6a4867905f480ca2d43c02f2c198ea", "22b1cb0cd8904ecc8f7dcfbca99d27a4", "b124a1bc0ccc46e0bd10e03595415358", "99567e7e1f994314bcf6a190e06a9817", "6352aa71801b4e97889ddcab35a9e453", "788e0cfd7db4483fb55591c77abb1720", "5e814939a89d4afcb80484fac513b5e1" ] }, "id": "9YnRBVweuZ-b", "outputId": "bcc5086e-b986-4911-ad2c-46367ac48aa7" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "NLI HEAD: Compositional Convolution (conv5d)\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "LOADING MODEL\n", "=================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "A new version of the following files was downloaded from https://huggingface.co/AbstractPhil/geolip-captionbert-8192:\n", "- modeling_caption_bert.py\n", ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/112 [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " from transformers import AutoModel, AutoTokenizer\n", " from datasets import load_dataset\n", "\n", " # ── Load model ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING MODEL\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", " tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", " for p in model.parameters():\n", " p.requires_grad = False\n", "\n", " has_bank = model.bank is not None\n", " print(f\" Model: {sum(p.numel() for p in model.parameters()):,} params (frozen)\")\n", " print(f\" Bank: {'present' if has_bank else 'absent'}\")\n", "\n", " # ── Load SNLI ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"LOADING SNLI\")\n", " print(f\"{'='*65}\")\n", "\n", " ds = load_dataset(\"stanfordnlp/snli\")\n", " train_ds = ds[\"train\"].filter(lambda x: x[\"label\"] >= 0)\n", " val_ds = ds[\"validation\"].filter(lambda x: x[\"label\"] >= 0)\n", " print(f\" Train: {len(train_ds):,} Val: {len(val_ds):,}\")\n", "\n", " MAX_TRAIN = 549000 # full SNLI\n", " MAX_VAL = 9800\n", "\n", " # ── Pre-encode ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PRE-ENCODING\")\n", " print(f\"{'='*65}\")\n", "\n", " @torch.no_grad()\n", " def encode_pairs(dataset, max_n, batch_size=256):\n", " dataset = dataset.select(range(min(max_n, len(dataset))))\n", " all_p_enr, all_h_enr = [], []\n", " all_labels = []\n", "\n", " for i in tqdm(range(0, len(dataset), batch_size), desc=\" Encoding\"):\n", " j = min(i + batch_size, len(dataset))\n", " batch = dataset[i:j]\n", "\n", " p_in = tokenizer(batch[\"premise\"], max_length=128,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_in)\n", "\n", " h_in = tokenizer(batch[\"hypothesis\"], max_length=128,\n", " padding=\"max_length\", truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " h_out = model(**h_in)\n", "\n", " p_feat = p_out.enriched if p_out.enriched is not None else p_out.last_hidden_state\n", " h_feat = h_out.enriched if h_out.enriched is not None else h_out.last_hidden_state\n", "\n", " all_p_enr.append(p_feat.cpu())\n", " all_h_enr.append(h_feat.cpu())\n", " all_labels.append(torch.tensor(batch[\"label\"]))\n", "\n", " return {\n", " \"p\": torch.cat(all_p_enr),\n", " \"h\": torch.cat(all_h_enr),\n", " \"labels\": torch.cat(all_labels),\n", " }\n", "\n", " train_data = encode_pairs(train_ds, MAX_TRAIN)\n", " val_data = encode_pairs(val_ds, MAX_VAL)\n", "\n", " d_enriched = train_data[\"p\"].shape[1]\n", " d_raw = 768\n", " d_bank = d_enriched - d_raw\n", " print(f\" Enriched: {d_enriched} (raw={d_raw} + bank={d_bank})\")\n", " print(f\" Train: {train_data['labels'].shape[0]:,} Val: {val_data['labels'].shape[0]:,}\")\n", "\n", " for label, name in [(0, \"entailment\"), (1, \"neutral\"), (2, \"contradiction\")]:\n", " n_l = (train_data[\"labels\"] == label).sum().item()\n", " print(f\" {name}: {n_l:,} ({n_l/len(train_data['labels']):.1%})\")\n", "\n", " del model; gc.collect(); torch.cuda.empty_cache()\n", "\n", " # Move to GPU\n", " train_p = train_data[\"p\"].to(DEVICE)\n", " train_h = train_data[\"h\"].to(DEVICE)\n", " train_labels = train_data[\"labels\"].to(DEVICE)\n", " val_p = val_data[\"p\"].to(DEVICE)\n", " val_h = val_data[\"h\"].to(DEVICE)\n", " val_labels = val_data[\"labels\"].to(DEVICE)\n", "\n", " # ── Build CompConv NLI ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"COMPOSITIONAL CONV NLI HEAD\")\n", " print(f\"{'='*65}\")\n", "\n", " nli = CompConvNLI(\n", " d_raw=d_raw, d_bank=max(d_bank, 1),\n", " d_path=256, n_components=5, n_classes=3, dropout=0.1\n", " ).to(DEVICE)\n", " n_head_params = sum(p.numel() for p in nli.parameters())\n", " print(f\" Head params: {n_head_params:,}\")\n", "\n", " # ── Training ──\n", " EPOCHS = 50\n", " BATCH = 1024\n", " LR = 1e-4\n", " n_train = train_labels.shape[0]\n", " n_batches = n_train // BATCH\n", "\n", " optimizer = torch.optim.AdamW(nli.parameters(), lr=LR, weight_decay=0.01)\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=n_batches * EPOCHS, eta_min=1e-6)\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(f\"TRAINING ({EPOCHS} epochs, {n_batches} batches/epoch)\")\n", " print(f\"{'='*65}\")\n", "\n", " best_val_acc = 0.0\n", " for epoch in range(EPOCHS):\n", " nli.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", " t0 = time.time()\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", "\n", " logits, _ = nli(train_p[idx], train_h[idx])\n", " labels = train_labels[idx]\n", " loss = F.cross_entropy(logits, labels)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(nli.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += len(idx)\n", "\n", " elapsed = time.time() - t0\n", " train_acc = total_correct / max(n, 1)\n", " train_loss = total_loss / max(n // BATCH, 1)\n", "\n", " # Validation\n", " nli.eval()\n", " with torch.no_grad():\n", " val_n = val_labels.shape[0]\n", " val_correct = 0\n", " val_loss_sum = 0\n", " all_preds, all_labs = [], []\n", " path_info = None\n", "\n", " for i in range(0, val_n, 512):\n", " j = min(i + 512, val_n)\n", " logits, info = nli(val_p[i:j], val_h[i:j])\n", " labs = val_labels[i:j]\n", " val_correct += (logits.argmax(-1) == labs).sum().item()\n", " val_loss_sum += F.cross_entropy(logits, labs, reduction=\"sum\").item()\n", " all_preds.append(logits.argmax(-1).cpu())\n", " all_labs.append(labs.cpu())\n", " if path_info is None:\n", " path_info = info\n", "\n", " val_acc = val_correct / val_n\n", " val_loss = val_loss_sum / val_n\n", " preds = torch.cat(all_preds)\n", " labs_all = torch.cat(all_labs)\n", "\n", " acc_ent = (preds[labs_all == 0] == 0).float().mean().item() if (labs_all == 0).sum() > 0 else 0\n", " acc_neu = (preds[labs_all == 1] == 1).float().mean().item() if (labs_all == 1).sum() > 0 else 0\n", " acc_con = (preds[labs_all == 2] == 2).float().mean().item() if (labs_all == 2).sum() > 0 else 0\n", "\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s\")\n", " print(f\" Task: loss={train_loss:.4f} t_acc={train_acc:.4f} v_acc={val_acc:.4f} v_loss={val_loss:.4f}\")\n", " print(f\" Per-class: ent={acc_ent:.3f} neu={acc_neu:.3f} con={acc_con:.3f}\")\n", " if path_info:\n", " top3 = path_info[\"top_paths\"][:3]\n", " path_str = \" \".join(f\"{comp}={w:.3f}\" for comp, w in top3)\n", " print(f\" Paths: {path_str} spread={path_info['weight_spread']:.4f}\")\n", " print(f\" Protos: sim={path_info.get('proto_spread', 0):.4f} \"\n", " f\"temp={path_info.get('temperature', 0):.2f}\")\n", "\n", " if val_acc > best_val_acc:\n", " best_val_acc = val_acc\n", " torch.save(nli.state_dict(), \"nli_conv5d_best.pt\")\n", " print(f\" ★ New best: {val_acc:.4f}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # PATH ANALYSIS\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"PATH WEIGHT ANALYSIS\")\n", " print(f\"{'='*65}\")\n", "\n", " nli.load_state_dict(torch.load(\"nli_conv5d_best.pt\", weights_only=True))\n", " nli.eval()\n", "\n", " weights = F.softmax(nli.path_weights, dim=0).cpu().tolist()\n", " ranked = sorted(zip(nli.compositions, weights), key=lambda x: -x[1])\n", " print(f\"\\n {'Path':<25} {'Weight':>8} {'Type':<15}\")\n", " print(f\" {'-'*50}\")\n", " for comp, w in ranked:\n", " if len(comp) == 1:\n", " ptype = \"holistic\"\n", " elif all(c == 1 for c in comp):\n", " ptype = \"independent\"\n", " elif comp[0] >= 3:\n", " ptype = \"geo-first\"\n", " elif comp[0] == 1 and sum(comp[1:]) == 4:\n", " ptype = \"geo→rest\"\n", " elif comp[0] == 2:\n", " ptype = \"geo+struct→...\"\n", " else:\n", " ptype = \"mixed\"\n", " bar = \"█\" * int(w * 100)\n", " print(f\" {str(comp):<25} {w:>8.4f} {ptype:<15} {bar}\")\n", "\n", " # ══════════════════════════════════════════════════════════════\n", " # COMPOSITIONAL ORDER TEST\n", " # ══════════════════════════════════════════════════════════════\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"COMPOSITIONAL ORDER TEST\")\n", " print(f\"{'='*65}\")\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", " label_names = [\"entailment\", \"neutral\", \"contradiction\"]\n", "\n", " test_pairs = [\n", " (\"a potato on top of a table\", \"a table on top of a potato\"),\n", " (\"a potato on top of a table\", \"there is a potato\"),\n", " (\"a cat is sitting on a mat\", \"a mat is sitting on a cat\"),\n", " (\"a dog chased the cat\", \"the cat chased the dog\"),\n", " (\"a woman is holding a baby\", \"a baby is holding a woman\"),\n", " (\"the boy kicked the ball\", \"the ball kicked the boy\"),\n", " (\"a man is riding a horse\", \"a horse is riding a man\"),\n", " (\"a girl is painting a picture\", \"a girl is creating art\"),\n", " (\"two dogs are playing in a park\", \"animals are outdoors\"),\n", " (\"a person is swimming in the ocean\", \"nobody is in the water\"),\n", " ]\n", "\n", " with torch.no_grad():\n", " for premise, hypothesis in test_pairs:\n", " p_in = tokenizer([premise], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " h_in = tokenizer([hypothesis], max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " p_out = model(**p_in)\n", " h_out = model(**h_in)\n", "\n", " p_feat = p_out.enriched if p_out.enriched is not None else p_out.last_hidden_state\n", " h_feat = h_out.enriched if h_out.enriched is not None else h_out.last_hidden_state\n", "\n", " logits, _ = nli(p_feat, h_feat)\n", " probs = F.softmax(logits, dim=-1)[0]\n", " pred = label_names[probs.argmax()]\n", "\n", " cos = F.cosine_similarity(\n", " p_out.last_hidden_state, h_out.last_hidden_state).item()\n", "\n", " print(f\"\\n P: {premise}\")\n", " print(f\" H: {hypothesis}\")\n", " print(f\" Pooled cos: {cos:.3f}\")\n", " print(f\" NLI: {pred} [E={probs[0]:.3f} N={probs[1]:.3f} C={probs[2]:.3f}]\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Best val accuracy: {best_val_acc:.4f}\")\n", " print(f\" Head params: {n_head_params:,}\")\n", " print(f\" Paths: {len(nli.compositions)}\")\n", " print(f\" Components: {nli.n_components} → d_path={nli.d_path}\")\n", " print(f\" Bank present: {has_bank}\")\n", " print(f\" Saved: nli_conv5d_best.pt\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "27627370b8284c5a862ffd5a3f953352", "338ff68f8d7445e7bfdb3cbbf8716d86", "449f0df7844148c59c86f1f6eccf2197", "a293658c27454a47aadd082610e78d06", "1f05373fe992499995c535ec94ab4404", "606a421f64114ed0aed8fc0437f0c171", "787e8cc4638e42318b2b93976b04893d", "5a76e50e8636432b85c6031fcb303f03", "c537d231252e4684a0727ec9171e696c", "da8eea806fcb4c9cbd0dbc3d1c6de5ad", "18edc062bd4d4c54a496b8c92e2f9d99", "eef3768972ff4b839344f03c681e057d", "a12a526b205c422dbc9d51765b12c2da", "df4b6755e47845538d30410ee1ffaef3", "88c92b8203c8464897ffec2683f1724b", "db0cfa00a74749eab32a13a3e81e9426", "4831efd2dcb14557af97e6f627a932ec", "08b0d9679dc24fa2a86a874c60b67d52", "99a3d94fb5dc457da2fa3968e51f2345", "15a95ffece5e419ab00699f951a71c03", "741ee9ffa3084edea972f8f09a877062", "d1a3d362dbf9426b9b2b8bb95b0abf52" ] }, "id": "EILZHZu4OE7v", "outputId": "cce90cb9-86d1-4614-b1ca-57e7fd67f25f" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "NLI HEAD: Compositional Convolution (conv5d)\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "LOADING MODEL\n", "=================================================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/112 [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BANK LOSS (differentiable, computed externally)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def compute_bank_loss(bank, embedding):\n", " \"\"\"\n", " Full refined bank loss — matches production train_alignment_bank.py exactly.\n", " All targets from bank's calibrated buffers. No hardcoded constants.\n", " Differentiable throughout — no .item() calls on loss components.\n", " \"\"\"\n", " B = embedding.shape[0]\n", " emb = embedding.float()\n", "\n", " # ── Per-expert full whitened Procrustes ──\n", " expert_cos_list = []\n", " expert_projected = []\n", " for i in range(bank.n_experts):\n", " R = bank.expert_rotations[i]\n", " W = bank.expert_whiteners[i]\n", " mu = bank.expert_means[i]\n", " centered = emb - mu\n", " whitened = centered @ W\n", " whitened_n = F.normalize(whitened, dim=-1)\n", " in_expert = whitened_n @ R.T\n", " back = in_expert @ R\n", " cos = F.cosine_similarity(whitened_n, back, dim=-1)\n", " expert_cos_list.append(cos)\n", " expert_projected.append(in_expert)\n", "\n", " expert_cos = torch.stack(expert_cos_list, dim=-1)\n", "\n", " # ── 1. Expert agreement ──\n", " expert_mean = expert_cos.mean(dim=-1, keepdim=True)\n", " l_agreement = (expert_cos - expert_mean).pow(2).mean()\n", "\n", " # ── 2. Rotation orthogonality ──\n", " l_ortho = 0.0\n", " for i in range(bank.n_experts):\n", " R = bank.expert_rotations[i]\n", " l_ortho += (R @ R.T - torch.eye(bank.d_embed, device=R.device)).pow(2).mean()\n", " l_ortho = l_ortho / bank.n_experts\n", "\n", " # ── 3. Anchor spread ──\n", " anchors_n = F.normalize(bank.anchors, dim=-1)\n", " anchor_sim = anchors_n @ anchors_n.T\n", " anchor_sim.fill_diagonal_(0)\n", " l_spread = anchor_sim.pow(2).mean()\n", "\n", " # ── 4. Anchor entropy (sharpness) ──\n", " anchor_cos = emb @ anchors_n.T\n", " anchor_probs = F.softmax(anchor_cos * 10, dim=-1)\n", " l_entropy = -(anchor_probs * (anchor_probs + 1e-12).log()).sum(-1).mean()\n", "\n", " # ── 5. Cross-expert differentiation ──\n", " cross_cos = []\n", " for i in range(bank.n_experts):\n", " for j in range(i + 1, bank.n_experts):\n", " cc = F.cosine_similarity(expert_projected[i], expert_projected[j], dim=-1)\n", " cross_cos.append(cc)\n", "\n", " if cross_cos:\n", " cross_features = torch.stack(cross_cos, dim=-1)\n", " l_cross_var = cross_features.var(dim=0).mean()\n", "\n", " # ── 6. Disagreement preservation (cross_cos + ratio) ──\n", " batch_cross_mean = cross_features.mean()\n", " batch_cross_std = cross_features.std()\n", " per_sample_agreement = expert_cos.mean(dim=-1)\n", " per_sample_disagreement = expert_cos.std(dim=-1)\n", " batch_disagree_ratio = (per_sample_disagreement / (per_sample_agreement + 1e-8)).mean()\n", " l_disagree = (\n", " (batch_cross_mean - bank.target_cross_cos_mean).pow(2) +\n", " (batch_cross_std - bank.target_cross_cos_std).pow(2) +\n", " (batch_disagree_ratio - bank.target_disagreement_ratio).pow(2)\n", " )\n", " else:\n", " l_cross_var = torch.tensor(0.0, device=emb.device)\n", " l_disagree = torch.tensor(0.0, device=emb.device)\n", "\n", " # ── 7. Embedding CV → should track bank.target_cv ──\n", " l_emb_cv = torch.tensor(0.0, device=emb.device)\n", " if B >= 10:\n", " emb_n = F.normalize(emb, dim=-1)\n", " vols = []\n", " for _ in range(16):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " emb_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " l_emb_cv = (emb_cv - bank.target_cv).abs()\n", "\n", " # ── Combined (same weights as production bank trainer) ──\n", " total = (1.0 * l_agreement +\n", " 1.0 * l_ortho +\n", " 0.5 * l_spread +\n", " 0.1 * l_entropy +\n", " 0.3 * l_cross_var +\n", " 0.3 * l_emb_cv +\n", " 0.5 * l_disagree)\n", "\n", " diagnostics = {\n", " \"agreement\": l_agreement.item(),\n", " \"ortho\": l_ortho.item() if torch.is_tensor(l_ortho) else l_ortho,\n", " \"spread\": l_spread.item(),\n", " \"entropy\": l_entropy.item(),\n", " \"cross_var\": l_cross_var.item(),\n", " \"disagree\": l_disagree.item(),\n", " \"emb_cv\": emb_cv.item() if B >= 10 else 0.0,\n", " \"expert_cos_mean\": expert_cos.mean().item(),\n", " \"expert_cos_std\": expert_cos.std().item(),\n", " }\n", "\n", " return total, diagnostics\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ALIGNMENT UTILITIES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " cos_before = F.cosine_similarity(Sc, Tc, dim=-1).mean().item()\n", " s_cov = (Sc.T @ Sc) / max(N_s - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_before\": cos_before, \"cos_after\": cos_after}\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run():\n", " torch.manual_seed(42)\n", " np.random.seed(42)\n", " names = [\"bert\", \"modern\", \"roberta\", \"albert\", \"distil\"]\n", "\n", " # ── Phase 0: Load cached embeddings ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 0: LOAD CACHED EMBEDDINGS\")\n", " print(f\"{'='*65}\")\n", "\n", " embeds = {}\n", " for name in names:\n", " p = os.path.join(CACHE_DIR, f\"{name}.pt\")\n", " embeds[name] = torch.load(p, weights_only=True)\n", " print(f\" {name}: {embeds[name].shape}\")\n", "\n", " caps_path = os.path.join(CACHE_DIR, \"captions.json\")\n", " with open(caps_path) as f:\n", " captions = json.load(f)\n", " N = min(len(captions), min(e.shape[0] for e in embeds.values()))\n", " print(f\" Captions: {len(captions):,}, using {N:,}\")\n", "\n", " # ── Phase 1: GPA Alignment ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 1: GPA ALIGNMENT\")\n", " print(f\"{'='*65}\")\n", "\n", " current = {name: embeds[name][:N].float() for name in names}\n", " for gpa_iter in range(15):\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", " total_delta = 0.0\n", " new_current = {}\n", " for name in names:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " total_delta += (new_current[name] - current[name]).pow(2).mean().item()\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter + 1) % 5 == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={total_delta:.8f}\")\n", " if total_delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\")\n", " break\n", "\n", " mean_shape = sum(current[n] for n in names) / len(names)\n", " aligned = {}\n", " for name in names:\n", " info = procrustes_align(embeds[name][:N], mean_shape)\n", " aligned[name] = apply_align(embeds[name][:N], info)\n", "\n", " consensus = F.normalize(sum(aligned[n] for n in names) / len(names), dim=-1)\n", " for name in names:\n", " c = F.cosine_similarity(consensus[:5000], aligned[name][:5000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {c:.4f}\")\n", "\n", " # Consensus CV\n", " consensus_cv = cv_metric(consensus[:5000].to(DEVICE))\n", " print(f\" Consensus CV: {consensus_cv:.4f}\")\n", "\n", " del embeds, aligned, current, mean_shape\n", " gc.collect()\n", "\n", " # ── Phase 2: Load model (unfrozen) ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 2: LOAD MODEL (unfrozen)\")\n", " print(f\"{'='*65}\")\n", "\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE)\n", " tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", "\n", " has_bank = model.bank is not None\n", " n_encoder = sum(p.numel() for n, p in model.named_parameters() if not n.startswith(\"bank.\"))\n", " n_bank = sum(p.numel() for n, p in model.named_parameters() if n.startswith(\"bank.\"))\n", " print(f\" Encoder: {n_encoder:,} params\")\n", " print(f\" Bank: {n_bank:,} params ({'present' if has_bank else 'absent'})\")\n", " print(f\" Total: {n_encoder + n_bank:,} params (ALL unfrozen)\")\n", "\n", " # ── Phase 3: Prepare data ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 3: TOKENIZE\")\n", " print(f\"{'='*65}\")\n", "\n", " captions = captions[:N]\n", " print(f\" Tokenizing {N:,} captions...\")\n", " tokens = tokenizer(captions, max_length=512, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\")\n", " input_ids = tokens[\"input_ids\"]\n", " attention_mask = tokens[\"attention_mask\"]\n", "\n", " n_train = N - N_VAL\n", " train_ids = input_ids[:n_train].to(DEVICE)\n", " train_mask = attention_mask[:n_train].to(DEVICE)\n", " train_targets = consensus[:n_train].to(DEVICE)\n", " val_ids = input_ids[n_train:n_train + N_VAL].to(DEVICE)\n", " val_mask = attention_mask[n_train:n_train + N_VAL].to(DEVICE)\n", " val_targets = consensus[n_train:n_train + N_VAL].to(DEVICE)\n", " print(f\" Train: {n_train:,} Val: {N_VAL:,}\")\n", "\n", " del tokens, input_ids, attention_mask\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", " # ── Phase 4: Training ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 4: CO-TRAIN (encoder + bank)\")\n", " print(f\"{'='*65}\")\n", "\n", " # Separate param groups: encoder gets lower LR\n", " encoder_params = [p for n, p in model.named_parameters() if not n.startswith(\"bank.\")]\n", " bank_params = [p for n, p in model.named_parameters() if n.startswith(\"bank.\")]\n", "\n", " optimizer = torch.optim.AdamW([\n", " {\"params\": encoder_params, \"lr\": LR_ENCODER},\n", " {\"params\": bank_params, \"lr\": LR_BANK},\n", " ], weight_decay=WEIGHT_DECAY)\n", "\n", " total_steps = (n_train // BATCH) * EPOCHS\n", " scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=WARMUP_STEPS),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - WARMUP_STEPS, 1), eta_min=1e-6)],\n", " milestones=[WARMUP_STEPS])\n", "\n", " best_val_cos = 0.0\n", " global_step = 0\n", " log_dir = f\"runs/cotrain_{time.strftime('%Y%m%d_%H%M%S')}\"\n", " writer = SummaryWriter(log_dir)\n", " print(f\" Tensorboard: {log_dir}\")\n", "\n", " # Log hyperparameters\n", " writer.add_text(\"config/model\", f\"encoder={n_encoder:,} bank={n_bank:,} total={n_encoder+n_bank:,}\")\n", " writer.add_text(\"config/training\", f\"epochs={EPOCHS} batch={BATCH} lr_enc={LR_ENCODER} lr_bank={LR_BANK}\")\n", " writer.add_text(\"config/losses\", f\"nce={W_NCE} mse={W_MSE} cv={W_CV} bank={W_BANK}\")\n", " writer.add_text(\"config/consensus\", f\"cv={consensus_cv:.4f}\")\n", "\n", " # Log initial bank state\n", " if has_bank:\n", " writer.add_scalar(\"bank_targets/cv\", model.bank.target_cv.item(), 0)\n", " writer.add_scalar(\"bank_targets/cross_cos_mean\", model.bank.target_cross_cos_mean.item(), 0)\n", " writer.add_scalar(\"bank_targets/cross_cos_std\", model.bank.target_cross_cos_std.item(), 0)\n", " writer.add_scalar(\"bank_targets/disagreement_ratio\", model.bank.target_disagreement_ratio.item(), 0)\n", "\n", " for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " n = 0\n", " t0 = time.time()\n", "\n", " pbar = tqdm(range(0, n_train, BATCH),\n", " desc=f\"E{epoch+1:2d}/{EPOCHS}\", unit=\"batch\")\n", " for i in pbar:\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", "\n", " out = model(train_ids[idx], train_mask[idx])\n", " emb = out.last_hidden_state\n", " tgt = train_targets[idx]\n", "\n", " # Student losses\n", " l_nce, acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=consensus_cv)\n", "\n", " # Bank losses\n", " if has_bank:\n", " l_bank, bank_diag = compute_bank_loss(model.bank, emb)\n", " else:\n", " l_bank = torch.tensor(0.0, device=DEVICE)\n", " bank_diag = {}\n", "\n", " loss = (W_NCE * l_nce + W_MSE * l_mse +\n", " W_CV * l_cv + W_BANK * l_bank)\n", "\n", " loss.backward()\n", " # Track gradient norms before clipping\n", " with torch.no_grad():\n", " enc_grad_norm = torch.nn.utils.clip_grad_norm_(\n", " [p for n, p in model.named_parameters() if not n.startswith(\"bank.\")],\n", " GRAD_CLIP)\n", " bank_grad_norm = torch.nn.utils.clip_grad_norm_(\n", " [p for n, p in model.named_parameters() if n.startswith(\"bank.\")],\n", " GRAD_CLIP) if has_bank else 0.0\n", "\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " with torch.no_grad():\n", " cos = F.cosine_similarity(emb, tgt, dim=-1).mean().item()\n", " cos_std = F.cosine_similarity(emb, tgt, dim=-1).std().item()\n", "\n", " global_step += 1\n", " n += 1\n", "\n", " # ── Step-level tensorboard (every 50 steps) ──\n", " if global_step % 50 == 0:\n", " # Losses\n", " writer.add_scalar(\"train_loss/total\", loss.item(), global_step)\n", " writer.add_scalar(\"train_loss/nce\", l_nce.item(), global_step)\n", " writer.add_scalar(\"train_loss/mse\", l_mse.item(), global_step)\n", " writer.add_scalar(\"train_loss/cv\", l_cv.item(), global_step)\n", " writer.add_scalar(\"train_loss/bank\", l_bank.item() if torch.is_tensor(l_bank) else l_bank, global_step)\n", "\n", " # Student metrics\n", " writer.add_scalar(\"train_student/cosine_mean\", cos, global_step)\n", " writer.add_scalar(\"train_student/cosine_std\", cos_std, global_step)\n", " writer.add_scalar(\"train_student/retrieval_acc\", acc, global_step)\n", "\n", " # Gradient norms\n", " writer.add_scalar(\"gradients/encoder_norm\", enc_grad_norm, global_step)\n", " if has_bank:\n", " writer.add_scalar(\"gradients/bank_norm\", bank_grad_norm, global_step)\n", "\n", " # Learning rates\n", " writer.add_scalar(\"lr/encoder\", optimizer.param_groups[0][\"lr\"], global_step)\n", " if len(optimizer.param_groups) > 1:\n", " writer.add_scalar(\"lr/bank\", optimizer.param_groups[1][\"lr\"], global_step)\n", "\n", " # Bank diagnostics\n", " if bank_diag:\n", " writer.add_scalar(\"train_bank/expert_agreement\", bank_diag[\"agreement\"], global_step)\n", " writer.add_scalar(\"train_bank/rotation_ortho\", bank_diag[\"ortho\"], global_step)\n", " writer.add_scalar(\"train_bank/anchor_spread\", bank_diag[\"spread\"], global_step)\n", " writer.add_scalar(\"train_bank/anchor_entropy\", bank_diag[\"entropy\"], global_step)\n", " writer.add_scalar(\"train_bank/cross_expert_var\", bank_diag[\"cross_var\"], global_step)\n", " writer.add_scalar(\"train_bank/disagreement_preserve\", bank_diag[\"disagree\"], global_step)\n", " writer.add_scalar(\"train_bank/emb_cv\", bank_diag[\"emb_cv\"], global_step)\n", " writer.add_scalar(\"train_bank/expert_cos_mean\", bank_diag[\"expert_cos_mean\"], global_step)\n", " writer.add_scalar(\"train_bank/expert_cos_std\", bank_diag[\"expert_cos_std\"], global_step)\n", "\n", " # ── Detailed step logging (every 500 steps) ──\n", " if global_step % 500 == 0 and has_bank:\n", " with torch.no_grad():\n", " # Per-expert round-trip cosines\n", " for exp_i in range(model.bank.n_experts):\n", " R = model.bank.expert_rotations[exp_i]\n", " W = model.bank.expert_whiteners[exp_i]\n", " mu = model.bank.expert_means[exp_i]\n", " centered = emb.float() - mu\n", " whitened = centered @ W\n", " whitened_n = F.normalize(whitened, dim=-1)\n", " in_expert = whitened_n @ R.T\n", " back = in_expert @ R\n", " exp_cos = F.cosine_similarity(whitened_n, back, dim=-1).mean().item()\n", " writer.add_scalar(f\"train_per_expert/cos_expert_{exp_i}\", exp_cos, global_step)\n", "\n", " # Rotation condition number\n", " sv = torch.linalg.svdvals(R)\n", " cond = (sv.max() / (sv.min() + 1e-8)).item()\n", " writer.add_scalar(f\"train_per_expert/rotation_cond_{exp_i}\", cond, global_step)\n", "\n", " # Cross-expert cosine matrix (10 pairs)\n", " expert_projected = []\n", " for exp_i in range(model.bank.n_experts):\n", " R = model.bank.expert_rotations[exp_i]\n", " W = model.bank.expert_whiteners[exp_i]\n", " mu = model.bank.expert_means[exp_i]\n", " wn = F.normalize((emb.float() - mu) @ W, dim=-1)\n", " expert_projected.append(wn @ R.T)\n", "\n", " pair_idx = 0\n", " expert_names = [\"bert\", \"modern\", \"roberta\", \"albert\", \"distil\"]\n", " for ei in range(model.bank.n_experts):\n", " for ej in range(ei + 1, model.bank.n_experts):\n", " cc = F.cosine_similarity(\n", " expert_projected[ei], expert_projected[ej], dim=-1).mean().item()\n", " writer.add_scalar(\n", " f\"train_cross_expert/{expert_names[ei]}_{expert_names[ej]}\", cc, global_step)\n", " pair_idx += 1\n", "\n", " # Anchor diagnostics\n", " anchors_n = F.normalize(model.bank.anchors, dim=-1)\n", " anchor_cos = emb.float() @ anchors_n.T\n", " writer.add_scalar(\"train_anchors/max_cos\", anchor_cos.max(dim=-1).values.mean().item(), global_step)\n", " writer.add_scalar(\"train_anchors/mean_cos\", anchor_cos.mean().item(), global_step)\n", " writer.add_scalar(\"train_anchors/top3_mean\",\n", " anchor_cos.topk(3, dim=-1).values.mean().item(), global_step)\n", "\n", " # Embedding space diagnostics\n", " pairwise = emb @ emb.T\n", " mask = ~torch.eye(emb.shape[0], dtype=torch.bool, device=DEVICE)\n", " writer.add_scalar(\"train_embedding/pairwise_cos_mean\", pairwise[mask].mean().item(), global_step)\n", " writer.add_scalar(\"train_embedding/pairwise_cos_std\", pairwise[mask].std().item(), global_step)\n", "\n", " # Spectral (on batch)\n", " centered_emb = emb.float() - emb.float().mean(0, keepdim=True)\n", " sv_emb = torch.linalg.svdvals(centered_emb)\n", " eff_dim = float((sv_emb.sum() ** 2) / (sv_emb.pow(2).sum() + 1e-12))\n", " writer.add_scalar(\"train_embedding/effective_dim\", eff_dim, global_step)\n", " writer.add_scalar(\"train_embedding/spectral_top1\",\n", " (sv_emb[0] / (sv_emb.sum() + 1e-8)).item(), global_step)\n", "\n", " d = max(n, 1)\n", " pbar.set_postfix(loss=f\"{loss.item():.4f}\",\n", " cos=f\"{cos:.3f}\",\n", " bank=f\"{l_bank.item() if torch.is_tensor(l_bank) else 0:.4f}\")\n", " pbar.close()\n", "\n", " elapsed = time.time() - t0\n", "\n", " # ── Epoch-level validation ──\n", " model.eval()\n", " with torch.no_grad():\n", " val_embs = []\n", " for vi in range(0, N_VAL, 512):\n", " vj = min(vi + 512, N_VAL)\n", " vo = model(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(vo.last_hidden_state)\n", " val_emb = torch.cat(val_embs)\n", " _, val_acc = infonce(val_emb[:2000], val_targets[:2000])\n", " val_cos = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", " val_cos_std = F.cosine_similarity(val_emb, val_targets, dim=-1).std().item()\n", " val_cv = cv_metric(val_emb[:2000])\n", "\n", " # Val spectral\n", " val_centered = val_emb[:2000].float() - val_emb[:2000].float().mean(0, keepdim=True)\n", " val_sv = torch.linalg.svdvals(val_centered)\n", " val_eff_dim = float((val_sv.sum() ** 2) / (val_sv.pow(2).sum() + 1e-12))\n", "\n", " # Val pairwise\n", " val_pair = val_emb[:500] @ val_emb[:500].T\n", " val_pair_mask = ~torch.eye(500, dtype=torch.bool, device=DEVICE)\n", " val_pairwise_mean = val_pair[val_pair_mask].mean().item()\n", "\n", " # Bank diagnostics on val\n", " if has_bank:\n", " _, val_bank_diag = compute_bank_loss(model.bank, val_emb[:2000])\n", "\n", " # Enriched diagnostics\n", " val_enriched_list = []\n", " for vi in range(0, min(N_VAL, 2000), 512):\n", " vj = min(vi + 512, min(N_VAL, 2000))\n", " vo = model(val_ids[vi:vj], val_mask[vi:vj])\n", " if vo.enriched is not None:\n", " val_enriched_list.append(vo.enriched)\n", " if val_enriched_list:\n", " val_enriched = torch.cat(val_enriched_list)\n", " geo_ctx = val_enriched[:, 768:]\n", " geo_ctx_sv = torch.linalg.svdvals(\n", " geo_ctx.float() - geo_ctx.float().mean(0, keepdim=True))\n", " geo_eff_dim = float((geo_ctx_sv.sum() ** 2) / (geo_ctx_sv.pow(2).sum() + 1e-12))\n", " geo_cv = cv_metric(F.normalize(geo_ctx[:1000], dim=-1))\n", " else:\n", " val_bank_diag = {}\n", "\n", " # ── Epoch tensorboard ──\n", " writer.add_scalar(\"val/cosine_mean\", val_cos, global_step)\n", " writer.add_scalar(\"val/cosine_std\", val_cos_std, global_step)\n", " writer.add_scalar(\"val/retrieval_acc\", val_acc, global_step)\n", " writer.add_scalar(\"val/cv\", val_cv, global_step)\n", " writer.add_scalar(\"val/effective_dim\", val_eff_dim, global_step)\n", " writer.add_scalar(\"val/pairwise_cos_mean\", val_pairwise_mean, global_step)\n", " writer.add_scalar(\"val/consensus_cv_target\", consensus_cv, global_step)\n", "\n", " if val_bank_diag:\n", " writer.add_scalar(\"val_bank/expert_agreement\", val_bank_diag[\"agreement\"], global_step)\n", " writer.add_scalar(\"val_bank/rotation_ortho\", val_bank_diag[\"ortho\"], global_step)\n", " writer.add_scalar(\"val_bank/anchor_spread\", val_bank_diag[\"spread\"], global_step)\n", " writer.add_scalar(\"val_bank/anchor_entropy\", val_bank_diag[\"entropy\"], global_step)\n", " writer.add_scalar(\"val_bank/emb_cv\", val_bank_diag[\"emb_cv\"], global_step)\n", " writer.add_scalar(\"val_bank/expert_cos_mean\", val_bank_diag[\"expert_cos_mean\"], global_step)\n", " writer.add_scalar(\"val_bank/expert_cos_std\", val_bank_diag[\"expert_cos_std\"], global_step)\n", " writer.add_scalar(\"val_bank/disagree_preserve\", val_bank_diag[\"disagree\"], global_step)\n", " writer.add_scalar(\"val_bank/cross_var\", val_bank_diag[\"cross_var\"], global_step)\n", "\n", " if val_enriched_list:\n", " writer.add_scalar(\"val_bank/geo_context_eff_dim\", geo_eff_dim, global_step)\n", " writer.add_scalar(\"val_bank/geo_context_cv\", geo_cv, global_step)\n", "\n", " writer.add_scalar(\"epoch/time_seconds\", elapsed, global_step)\n", " writer.add_scalar(\"epoch/number\", epoch + 1, global_step)\n", "\n", " # ── Console print ──\n", " print(f\"\\n E{epoch+1:2d}: {elapsed:.0f}s step={global_step}\")\n", " print(f\" Student: v_cos={val_cos:.4f}±{val_cos_std:.4f} \"\n", " f\"v_acc={val_acc:.3f} v_cv={val_cv:.4f} eff_dim={val_eff_dim:.1f}\")\n", " print(f\" Losses: nce={l_nce.item():.4f} mse={l_mse.item():.4f} \"\n", " f\"bank={l_bank.item() if torch.is_tensor(l_bank) else 0:.4f}\")\n", " if val_bank_diag:\n", " print(f\" Bank: agr={val_bank_diag['agreement']:.6f} \"\n", " f\"ortho={val_bank_diag['ortho']:.6f} \"\n", " f\"entropy={val_bank_diag['entropy']:.4f} \"\n", " f\"emb_cv={val_bank_diag['emb_cv']:.4f}\")\n", " print(f\" exp_cos={val_bank_diag['expert_cos_mean']:.3f}±\"\n", " f\"{val_bank_diag['expert_cos_std']:.3f} \"\n", " f\"disagree={val_bank_diag['disagree']:.6f} \"\n", " f\"spread={val_bank_diag['spread']:.5f}\")\n", " if val_enriched_list:\n", " print(f\" Context: geo_eff_dim={geo_eff_dim:.1f} geo_cv={geo_cv:.4f}\")\n", "\n", " if val_cos > best_val_cos:\n", " best_val_cos = val_cos\n", " torch.save(model.state_dict(), \"cotrain_best.pt\")\n", " print(f\" ★ New best: v_cos={val_cos:.4f}\")\n", "\n", " torch.save(model.state_dict(), \"cotrain_final.pt\")\n", " writer.close()\n", "\n", " # ── Phase 5: Verification ──\n", " print(f\"\\n{'='*65}\")\n", " print(\"PHASE 5: VERIFICATION\")\n", " print(f\"{'='*65}\")\n", "\n", " model.load_state_dict(torch.load(\"cotrain_best.pt\", weights_only=True))\n", " model.eval()\n", "\n", " with torch.no_grad():\n", " val_embs = []\n", " for vi in range(0, N_VAL, 512):\n", " vj = min(vi + 512, N_VAL)\n", " vo = model(val_ids[vi:vj], val_mask[vi:vj])\n", " val_embs.append(vo.last_hidden_state)\n", " val_emb = torch.cat(val_embs)\n", " _, val_acc = infonce(val_emb[:2000], val_targets[:2000])\n", " val_cos = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", " val_cv = cv_metric(val_emb[:2000])\n", "\n", " # Check enriched\n", " sample_out = model(val_ids[:10], val_mask[:10])\n", " if sample_out.enriched is not None:\n", " print(f\" Enriched: {sample_out.enriched.shape}\")\n", " print(f\" Geo: {sample_out.geometric_context}\")\n", "\n", " # Quick similarity test\n", " test_texts = [\n", " \"A cat sitting on a windowsill\",\n", " \"A dog playing in the park\",\n", " \"A still life painting with flowers\",\n", " \"A child riding a bicycle\",\n", " ]\n", " with torch.no_grad():\n", " test_in = tokenizer(test_texts, max_length=128, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " test_out = model(**test_in)\n", " test_emb = test_out.last_hidden_state\n", " sim = test_emb @ test_emb.T\n", " print(f\"\\n Pairwise cosines:\")\n", " for i in range(len(test_texts)):\n", " for j in range(i+1, len(test_texts)):\n", " print(f\" [{i}]↔[{j}]: {sim[i,j]:.3f} ({test_texts[i][:30]} ↔ {test_texts[j][:30]})\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"SUMMARY\")\n", " print(f\"{'='*65}\")\n", " print(f\" Best v_cos: {best_val_cos:.4f}\")\n", " print(f\" Final v_cv: {val_cv:.4f}\")\n", " print(f\" Consensus CV: {consensus_cv:.4f}\")\n", " print(f\" Val R@1: {val_acc:.3f}\")\n", " print(f\" Encoder LR: {LR_ENCODER}\")\n", " print(f\" Bank LR: {LR_BANK}\")\n", " print(f\" Bank weight: {W_BANK}\")\n", " print(f\"\\n Saved: cotrain_best.pt, cotrain_final.pt\")\n", " print(f\" Tensorboard: {log_dir}\")\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "d677f32ed3144b6c89f299e88bdd91db", "89d9aa5b15c443798086d9b42ce6031e", "a113c72f2b2c403c991c205293f7baa3", "b8e7f333003c453eb9d4c85d726f56f5", "85f4a11c50cc42c9a9e0c9012d72ac04", "0cd8990f98434e1c9f1afdcf6f54a4e9", "405998fe651449adaaff88784c1f6c35", "1c6231d900844c57b2a0e41d3491d824", "a8d419d176c645c4816b9297f15add8f", "db04b5a4ac844ee98560ec1ad10e4fbd", "9ef880c5e59e4bc8b525b782de253c83" ] }, "id": "yjQ1CUaE8BX5", "outputId": "a7aebd99-389d-44bf-cee4-561f569634bb" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "CO-TRAIN: Student + Alignment Bank (unfrozen)\n", "=================================================================\n", " Device: cuda\n", " LR encoder: 0.0001 LR bank: 0.0005\n", " Bank weight: 0.2\n", "\n", "=================================================================\n", "PHASE 0: LOAD CACHED EMBEDDINGS\n", "=================================================================\n", " bert: torch.Size([500000, 768])\n", " modern: torch.Size([500000, 768])\n", " roberta: torch.Size([500000, 768])\n", " albert: torch.Size([500000, 768])\n", " distil: torch.Size([500000, 768])\n", " Captions: 500,000, using 500,000\n", "\n", "=================================================================\n", "PHASE 1: GPA ALIGNMENT\n", "=================================================================\n", " GPA iter 1: delta=1.99174462\n", " GPA iter 5: delta=0.00009400\n", " GPA iter 10: delta=0.00001988\n", " GPA iter 15: delta=0.00000849\n", " cos(consensus, bert): 0.9880\n", " cos(consensus, modern): 0.9831\n", " cos(consensus, roberta): 0.9885\n", " cos(consensus, albert): 0.9864\n", " cos(consensus, distil): 0.9909\n", " Consensus CV: 0.2543\n", "\n", "=================================================================\n", "PHASE 2: LOAD MODEL (unfrozen)\n", "=================================================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/112 [00:00